21 Lora Jobs

Technical Leadership Schematic Design Development boards Hardware Development RF Design & Wireless Tech: Hands-on exp. with RF layout practices, impedance matching, antenna selection/integration, (LoRa, BLE, Wi-Fi, GSM, NB-IoT, GPS). EDA Tools Required Candidate profile Bachelor or Master's Electrical Eng, Electronics Eng. related field. Min 6-7 years exp electronics hardware design, development, with focus on schematic design. Basic knowledge of Embedded-C, Python

Role & responsibilities 1. Test Execution: Conduct functional, performance, environmental, and mechanical tests for IoT products. Perform manual and automated testing for hardware and software components. Set up and manage test equipment and environments as per the validation plan. 2. Documentation & Reporting: Record and analyze test results, highlighting issues and potential improvements. Prepare detailed test reports for internal and external stakeholders. 3. Defect Identification & Resolution: Identify, replicate, and document defects during testing. Collaborate with product development teams to resolve identified issues. 4. Field Testing: Assist in real-world field tests to validate product performance under actual use conditions. Collect and analyze field test data to support product validation. 5. Compliance Testing Support: Work with the Product Validation Manager and external labs to conduct compliance testing. Ensure adherence to relevant industry standards and certification requirements. 6. Continuous Improvement: Suggest improvements in testing methodologies and tools. Stay updated on industry trends, tools, and best practices in IoT product testing. Key Skills: Functional and Environmental Testing IoT Product Testing Techniques Problem-Solving and Analytical Skills Test Equipment Setup and Management Test Data Analysis and Reporting Communication and Collaboration Skills Familiarity with Agile and Waterfall methodologies Hands-on experience with testing tools and frameworks Preferred Skills: Knowledge of IoT systems and communication protocols (e.g., Wi-Fi, Bluetooth, LoRa). Experience with automated testing frameworks. Basic understanding of mechanical components and design validation. Working Conditions: Lab-based role with occasional field testing. Collaborative work environment with cross-functional teams.

The Role. The ideal candidate for this role will be an innovative self-starter. You will be an AI expert with experience in making architectural tradeoffs to transform AI performance for a variety of use cases. You will collaborate with internal and external development engineers (architecture, hardware, validation, software services). You will contribute to development, support device characterization and benchmarking :. Proficiency in Large Models & Deep Neural Networks. Hands-on experience in working with large models & deep neural networks. Expertise in LLMs with working knowledge of large language models (LLMs). Extensive experience in System platform Architecture. Experience in Development Preferable for memory/storage/ any embedded system. In depth knowledge and extensive experience in dealing with Standardizations/Technical Papers/Patents. Extensive experience with C/C++ and Python programming. Develop and fine-tune LLMs (GPT, Llama, Mistral, Falcon, Claude, etc.) for domain-specific applications. Implement RAG pipelines using LlamaIndex, LangGraph, and vector databases (FAISS, Pinecone, Weaviate, ChromaDB) to enhance response accuracy. Build AI-powered chatbots and autonomous agents using LangGraph, CrewAI, LlamaIndex, and OpenAI APIs. Optimize and deploy generative AI models for real-time inference using cloud platforms (AWS, GCP, Azure) and MLOps tools (Docker, Kubernetes, MLflow). Fine-tune models using LoRA, QLoRA, PEFT, and RLHF to improve efficiency and personalization. Develop AI-driven workflows for structured reasoning and decision-making using CrewAI and LangGraph. Integrate multi-modal AI models (text, image, speech) into enterprise solutions. Implement memory and retrieval strategies for LLM-based systems using vector search and caching techniques. Ensure AI models follow ethical AI guidelines, bias mitigation, and security best :. Tech in Computer Science, Electrical Engineering. 8 to 10 Years of experience in relevant domain. Strong analytical & abstract thinking ability as well as technical communication skills. Able to work independently and perform in fast paced environment. Ability to troubleshoot and debug complex issues. Working Knowledge on Device Driver is desirable. Prior experience in working with Skills :. Languages & Frameworks Proficiency in Python, PyTorch, TensorFlow, JAX. (ref:hirist.tech).

Role & responsibilities Key Skills required : Generative AI, Multi Modal AI Creative AI solutions and workflows across all creative content types including Copy/Text, Imagery, Key Visuals, Characters, Avatars, Audio, Speech and Video AI Creative AI Automation workflows with content creation and content editing at scale, using AI services and AI APIs Experience with multiple Multi Modal AI Foundation Models LLM, LLM App Dev AI Agents, Agentic AI Workflows Responsibilities : Design and build web apps and solutions that leverage Creative AI Services, Multi Modal AI models, and Generative AI workflows Leverage Multi modal AI capabilities supporting all content types and modalities, including text, imagery, audio, speech and video Build creative automation workflows that help produce creative concepts, creative production deliverables, and integrated creative outputs, leveraging AI and Gen-AI models Integrate AI Image Gen Models and AI Image Editing models from key technology partners Integrate Text / Copy Gen Models for key LLM providers Integrate Speech / Audio Gen and Editing models for use cases such as transcription, translation, and AI generated audio narration Integrate AI enabled Video Gen and Video Editing models Fine-Tune Multi Modal AI models for brand specific usage and branded content generation Constantly Research and explore emerging trends and techniques in the field of generative AI and LLMs to stay at the forefront of innovation. Drive product development and delivery within tight timelines Collaborate with full-stack developers, engineers, and quality engineers, to develop and integrate solutions into existing enterprise products. Collaborate with technology leaders and cross-functional teams to develop and validate client requirements and rapidly translate them into working solutions. Develop, implement and optimize scalable AI-enabled products Integrate Gen-AI and Multi Modal AI solutions into Cloud Platforms, Cloud Native Apps, and custom Web Apps Execute implementation across all layers of the application stack including front-end, back-end, APIs, data and AI services Build enterprise products and full-stack applications on the MERN + Python stack, with a clear separation of concerns across layers Skills and Competencies: Deep Hands-on Experience in Multi modal AI models and tools. Hands-on Experience in API integration with AI services Multi Modal AI – competencies : Hands-on Experience with intelligent document processing and document indexing + document content extraction and querying, using multi modal AI Models Hands-on Experience with using Multi modal AI models and solutions for Imagery and Visual Creative – including text-to-image, image-to-image, image composition, image variations, etc. Hands-on Experience with popular AI Image Composition and Editing models from providers such as Adobe Firefly, Getty Images, ShutterStock, Flux and Flux Pro, and Stable Diffusion, and the ability to integrate them programmatically over API calls and workflows Hands-on Experience with Computer Vision and Image Processing using Multi-modal AI – for use cases such as object detection, automated captioning, automated masking, and image segmentation – again all done programmatically over API calls and Workflows Hands-on Experience with using Multi modal AI for Speech – including Text to Speech, Speech to Text, and use of Pre-built vs. Custom Voices Hands-on Experience with building Voice-enabled and Voice-activated experiences, using Speech AI and Voice AI solutions Hands-on Experience with AI Character and AI Avatar development, using a variety of different tools and platforms Fine-Tuning Creative AI Content models for Custom Styles, Custom Characters, and Custom Brand specific imagery Fine-Tuning Speech Models for Custom Voices Good understanding of advanced fine-tuning techniques such as LoRA Ability to execute and run fine-tuning workflows, end-to-end, in particular for Image Gen and Image Editing models Hands-on Experience with leveraging APIs to orchestrate across Multi Modal AI models Hands-on Experience with building workflows that orchestrate across Multi Modal AI models Good Experience with using AI Assistants to drive natural language interactions and orchestration with Multi Modal AI models Good Experience with use of AI Agents and Agentic AI workflows to drive dynamic orchestration across Multi Modal AI services and models Programming Skills : Good Expertise in MERN stack (JavaScript) including client-side and server-side JavaScript Good Expertise in Python based development, including Python App Dev for Multi Modal AI Integration Well-rounded in both programming languages Strong experience in client-side JavaScript Apps and building Static Web Apps + Dynamic Web Apps both in JavaScript Hands-on Experience in front-end and back-end development Minimum 2+ years hands-on experience in working with Full-Stack MERN apps, using both client-side and server-side JavaScript Minimum 2 years hands-on experience in Python development Minimum 2 years hands-on experience in working with LLMs and LLM models, using Python LLM Dev Skills : Solid Hands-on Experience with building end-to-end RAG pipelines and custom AI indexing solutions to ground LLMs and enhance LLM output Good Experience with building AI and LLM enabled Workflows Hands-on Experience integrating LLMs with external tools such as Web Search Ability to leverage advanced concepts such as tool calling and function calling, with LLM models Hands-on Experience with Conversational AI solutions and chat-driven experiences Experience with multiple LLMs and models – primarily GPT-4o, GPT o1, and o3 mini, and preferably also Gemini, Claude Sonnet, etc. Experience and Expertise in Cloud Gen-AI platforms, services, and APIs, primarily Azure OpenAI, and perferably also AWS Bedrock , and/or GCP Vertex AI . Hands-on Experience with Assistants and the use of Assistants in orchestrating with LLMs Hands-on Experience working with AI Agents and Agent Services . Nice-to-Have capabilities (Not essential) : Hands-on Experience with building Agentic AI workflows that enable iterative improvement of output Hands-on experience with both Single-Agent and Multi-Agent Orchestration solutions and frameworks Hands-on experience with different Agent communication and chaining patterns Ability to leverage LLMs for Reasoning and Planning workflows, that enable higher order “goals” and automated orchestration across multiple apps and tools Ability to leverage Graph Databases and “Knowledge Graphs” as an alternate method / replacement of Vector Databases, for enabling more relevant semantic querying and outputs via LLM models. Good Background with Machine Learning solutions Good foundational understanding of Transformer Models Good foundational understanding of Diffusion Models Some Experience with custom ML model development and deployment is desirable. Proficiency in deep learning frameworks such as PyTorch, or Keras. Experience with Cloud ML Platforms such as Azure ML Service, AWS Sage maker, and NVidia AI Foundry.

Job Description: ENERGY7 is seeking a passionate and skilled IoT Engineer to join our team working on cutting-edge projects in remote diagnostics, predictive maintenance, and real-time monitoring for Indian Railways and global clients. You will be responsible for designing, developing, and deploying end-to-end IoT solutions, integrating hardware, firmware, and cloud-based analytics. Key Responsibilities: Design and develop IoT devices using microcontrollers (ESP32, STM32, etc.) Interface sensors and actuators via RS-485, UART, I2C, SPI, Modbus, LoRa, Zigbee, or LTE. Develop firmware in C/C++/MicroPython for real-time data acquisition and control. Integrate with cloud platforms (AWS, Azure, or custom MQTT/HTTP/REST endpoints). Collaborate with data science and software teams to ensure reliable data flow and system scalability. Participate in system testing, field deployment, debugging, and performance tuning. Create technical documentation and provide support during installation and maintenance. Desired Candidate Profile: Bachelor's/Master's in Electronics, Instrumentation, Computer Science, or related field. 2-5 years of experience in IoT hardware design and firmware development. Proficiency in embedded C/C++, RTOS, or FreeRTOS. Familiarity with industrial communication protocols like Modbus RTU/TCP, MQTT, RS-485, etc. Experience in PCB design and hardware debugging (preferred). Working knowledge of cloud platforms or edge computing (Jetson, Raspberry Pi, etc.) is a plus. Strong problem-solving, documentation, and communication skills. Nice to Have: Experience with predictive maintenance or condition monitoring systems. Knowledge of AI/ML or data pipelines for sensor data. Exposure to Indian Railway signalling systems or industrial automation setups.

Key Responsibilities: Research & Innovation: Conduct applied research in generative AI, foundation models, NLP, computer vision, and multimodal AI. Stay abreast of the latest publications and open-source advancements. Model Development: Fine-tune, evaluate, and optimize large language models (LLMs), transformers, and other generative models for specific business and product use cases. Prototyping & Experimentation: Build proof-of-concepts and experimental systems that demonstrate the potential of GenAI across domains such as content generation, summarization, synthetic data, agent systems, etc. Data & Evaluation Pipelines: Design robust data pipelines, evaluation metrics, and benchmarking systems to validate model performance, safety, and bias. Collaboration: Work with cross-functional teams including product managers, ML engineers, and data scientists to translate research into production-grade systems. Open Source & IP Contribution: Publish findings in peer-reviewed venues, contribute to open-source projects, or generate intellectual property relevant to the business. Required Qualifications: 57 years of experience in machine learning or applied AI roles, with at least 23 years working on generative models or related research. Strong foundation in deep learning frameworks such as PyTorch, TensorFlow, or JAX. Experience with LLMs (e.g., GPT, LLaMA, Claude), diffusion models, or vision-language models. Proficient in Python and ML tools/libraries such as Hugging Face Transformers, LangChain, or similar. Understanding of responsible AI practices, bias mitigation, and model explainability. Master's or PhD in Computer Science, Machine Learning, Mathematics, or related fields. Preferred Qualifications: Experience with open-source LLMs or fine-tuning techniques like LoRA, PEFT, RLHF, etc. Knowledge of MLOps practices and deployment of models in production (e.g., via Kubernetes, Ray, Triton).

Job Title: Firmware and Embedded Engineer Key Responsibilities: Firmware Development: Develop and maintain firmware for microcontroller-based devices using FreeRTOS. Linux System Development: Design and implement Yocto-based Linux systems, ensuring stability and efficiency. Device Driver Development: Create custom device drivers for BLE, LoRa, and other communication protocols. Sensor Data Management: Develop firmware for efficient processing and management of sensor data. Embedded Machine Learning: Implement and optimize embedded ML algorithms for predictive analytics and real-time decision-making. Testing & Debugging: Conduct comprehensive testing, including unit, integration, and system-level testing, and perform debugging to ensure the highest quality of firmware. Cross-Functional Collaboration: Work closely with hardware engineers and software developers to ensure seamless integration of firmware with other system components. Documentation & Compliance: Maintain detailed documentation of all firmware development processes and ensure compliance with relevant industry standards. Qualifications: Bachelors or Masters degree in Computer Engineering, Electrical Engineering, or a related field. 7+ years of experience in firmware and embedded system development. Proficiency in FreeRTOS for microcontroller-based systems. Experience in developing Yocto-based Linux systems. Skilled in creating custom device drivers, particularly for BLE and LoRa. Experience with Digital Signal Processing, Filter Design and RF design. Knowledge in processing sensor data and implementing embedded machine learning algorithms. Strong troubleshooting and problem-solving skills. Excellent written and verbal communication skills. Preferred Skills: Familiarity with IoT device development, particularly in pet wellness or consumer electronics. Experience with wireless and wired communication protocols. Proven ability to collaborate effectively in a multidisciplinary team environment.

Job Description Summary role description: Hiring for a Solution Architect for an InsurTech platform provider, Life and Health Insurance. Company description: Our client is a VC-funded InsurTech platform company, providing software platforms for Life Insurance and Health Insurance companies across the globe. Leveraging their domain expertise, regulatory knowledge and technology experience, they architect innovative products and disrupt the Insurance value chain from Customer Acquisition to Engagement. Their products serve customers across the APAC region. Role details: Title / Designation : Solutions Architect Location: Pune/Mumbai Work Mode: Work from office Role & responsibilities: Define and evolve AI/ML architecture roadmap for FWA, IDP, and Agentic AI frameworks. Lead technical presentations and solution design sessions with customers. Design scalable architectures for multi-agent systems and autonomous decision-making. Drive innovation by evaluating emerging AI/ML technologies, especially AI agents. Architect cloud-native platforms supporting the complete AI/ML lifecycle. Provide technical leadership across product development and customer implementation. Collaborate with data scientists, engineers, and business stakeholders. Stay at the forefront of AI/ML innovations, particularly autonomous agents and LLMs. Establish and enforce technical standards and architectural guidelines. Candidate requirements: 10+ years in software architecture/system design in insurance domain, with 5+ years in AI/ML systems/platforms. Proven experience delivering large-scale AI/ML solutions, preferably with autonomous agents. Experience with cloud-native architectures (AWS, Azure, GCP), containerization (Docker, Kubernetes), and microservices. Deep expertise in AI/ML system architecture (model serving, MLOps/LLMOps pipelines, distributed computing). Strong understanding of Agentic AI, multi-agent systems, and LLMs (including LoRA, PEFT fine-tuning). Bachelor's or Master's in CS, SE, Data Science, or related technical field. Exceptional technical leadership and communication skills. Selection process: Interview with Senior Solution Architect Interview with CTO HR Discussion Check Your Resume for Match Upload your resume and our tool will compare it to the requirements for this job like recruiters do.

Role & responsibilitiMes Candiate needs to be 8+ Years of Experience Details on tech stack Python Prompt engineering Best practices for prompt engineering How LLM can be used in applications for a variety of tasks NLP Understanding of typical NLP problems: classification, NER, summarization, question answering, sentiment analysis, etc. Theoretical intuitive understanding of how Transformers work (tokenization, attention, etc). Word and sentence embeddings Vector search Vector databases, performance tuning Document chunking techniques LLM applications development LangChain, LlamaIndex Chain of Thoughts, DSP, and other techniques Agents and tools Google cloud (GCP) Nice to have requirements to the candidate Preferable, the engineers are expected to have IT services/consulting experience. Proficient in developing LLM-powered systems using advanced prompt engineering techniques, RAG and agentic design patterns. Experienced with frameworks like LangChain, LlamaIndex, and DSPy. Familiar with evaluation approaches and metrics for different types of LLM-based systems. Experienced with keyword and vector search methods, including understanding of their underlying algorithms. Familiar with popular vector search engines. Competent in various document understanding models and techniques to parse complex documents and implement effective chunking strategies for RAG systems. Familiar with LLM and embedding models fine-tuning techniques. Competent in using joint vision-language and generative models to solve various problems related to image generation, visual question answering, and multi-modal search. Familiar with diffusion models and associated techniques like LoRA, Dreambooth, and ControlNet. Understanding of the challenges and risks associated with the development of Generative AI systems and how to mitigate them. Familiar with various architecture design patterns for different types of LLM-based applications such as chatbots, text2sql, document understanding, etc. Familiar with various approaches to scalability and cost reduction in Generative AI systems. Ability to stay updated with the latest advancements in Generative AI and integrate emerging technologies to drive innovation and improve the performance of AI systems. Familiar with Responsible AI principles and Human-AI interaction design best practices. Preferred candidate profile

Company Name - Fission Labs Apply Here - https://app.fabrichq.ai/jobs/0e46cebe-8b91-4a96-9061-950b66dc4d54 About Us: Headquartered in Sunnyvale, with offices in Dallas & Hyderabad, Fission Labs is a leading software development company, specializing in crafting flexible, agile, and scalable solutions that propel businesses forward.With a comprehensive range of services, including product development, cloud engineering, big data analytics, QA, DevOps consulting, and AI/ML solutions, we empower clients to achieve sustainable digital transformation that aligns seamlessly with their business goals. Key Responsibilities Design and architect complex Generative AI solutions using AWS technologies Develop advanced AI architectures incorporating state-of-the-art GenAI technologies Create and implement Retrieval Augmented Generation (RAG) and GraphRAG solutions Architect scalable AI systems using AWS Bedrock and SageMaker Design and implement agentic AI systems with advanced reasoning capabilities Develop custom AI solutions leveraging vector databases and advanced machine learning techniques Evaluate and integrate emerging GenAI technologies and methodologies Technical Expertise Requirements Generative AI Technologies Expert-level understanding of: Retrieval Augmented Generation (RAG) Vector Database architectures Agentic AI design principles AWS AI Services Comprehensive expertise in: AWS Bedrock Amazon SageMaker AWS AI/ML services ecosystem Cloud-native AI solution design Technical Skills Advanced Python programming for AI/ML applications

Company Name - Fission Labs Apply Here - https://app.fabrichq.ai/jobs/0e46cebe-8b91-4a96-9061-950b66dc4d54 About Us: Headquartered in Sunnyvale, with offices in Dallas & Hyderabad, Fission Labs is a leading software development company, specializing in crafting flexible, agile, and scalable solutions that propel businesses forward.With a comprehensive range of services, including product development, cloud engineering, big data analytics, QA, DevOps consulting, and AI/ML solutions, we empower clients to achieve sustainable digital transformation that aligns seamlessly with their business goals. Key Responsibilities Design and architect complex Generative AI solutions using AWS technologies Develop advanced AI architectures incorporating state-of-the-art GenAI technologies Create and implement Retrieval Augmented Generation (RAG) and GraphRAG solutions Architect scalable AI systems using AWS Bedrock and SageMaker Design and implement agentic AI systems with advanced reasoning capabilities Develop custom AI solutions leveraging vector databases and advanced machine learning techniques Evaluate and integrate emerging GenAI technologies and methodologies Technical Expertise Requirements Generative AI Technologies Expert-level understanding of: Retrieval Augmented Generation (RAG) Vector Database architectures Agentic AI design principles AWS AI Services Comprehensive expertise in: AWS Bedrock Amazon SageMaker AWS AI/ML services ecosystem Cloud-native AI solution design Technical Skills Advanced Python programming for AI/ML applications

Conceptualize, design, and produce engaging and high-quality videos using AI video generation tools like InVideo AI, Runway ML, Kling AI, and other relevant software.A strong portfolio showcasing your AI-generated video work is essential.

Roles and Responsibilities: As a, Associate Manager - Senior Data scientist you will solve some of the most impactful business problems for our clients using a variety of AI and ML technologies. You will collaborate with business partners and domain experts to design and develop innovative solutions on the data to achieve predefined outcomes. - Engage with clients to understand current and future business goals and translate business problems into analytical frameworks - Develop custom models based on in-depth understanding of underlying data, data structures, and business problems to ensure deliverables meet client needs - Create repeatable, interpretable and scalable models - Effectively communicate the analytics approach and insights to a larger business audience - Collaborate with team members, peers and leadership at Tredence and client companies Qualification: - Bachelor's or Master's degree in a quantitative field (CS, machine learning, mathematics, statistics) or equivalent experience. - 5+ years of experience in data science, building hands-on ML models - Experience with LMs (Llama (1/2/3), T5, Falcon, Langchain or framework similar like Langchain) - Candidate must be aware of entire evolution history of NLP (Traditional Language Models to Modern Large Language Models), training data creation, training set-up and finetuning - Candidate must be comfortable interpreting research papers and architecture diagrams of Language Models - Candidate must be comfortable with LORA, RAG, Instruct fine-tuning, Quantization, etc. - Experience leading the end-to-end design, development, and deployment of predictive modeling solutions. - Excellent programming skills in Python. Strong working knowledge of Python's numerical, data analysis, or AI frameworks such as NumPy, Pandas, Scikit-learn, Jupyter, etc. - Advanced SQL skills with SQL Server and Spark experience. - Knowledge of predictive/prescriptive analytics including Machine Learning algorithms (Supervised and Unsupervised) and deep learning algorithms and Artificial Neural Networks - Experience with Natural Language Processing (NLTK) and text analytics for information extraction, parsing and topic modeling. - Excellent verbal and written communication. Strong troubleshooting and problem-solving skills. Thrive in a fast-paced, innovative environment - Experience with data visualization tools -- PowerBI, Tableau, R Shiny, etc. preferred - Experience with cloud platforms such as Azure, AWS is preferred but not required. Required Skills g, Pyspark, Python, SQL, GenAi, NLP, Clasical ML, DL, Supervised ML, Transformer Models, Transformers, Unsupervised ML

Job Description: Graduate degree in a quantitative field (CS, statistics, applied mathematics, machine learning, or related discipline) - Good programming skills in Python with strong working knowledge of Python's numerical, data analysis, or AI frameworks such as NumPy, Pandas, Scikit-learn, etc. - Experience with LMs (Llama (1/2/3), T5, Falcon, Langchain or framework similar like Langchain) - Candidate must be aware of entire evolution history of NLP (Traditional Language Models to Modern Large Language Models), training data creation, training set-up and finetuning - Candidate must be comfortable interpreting research papers and architecture diagrams of Language Models - Candidate must be comfortable with LORA, RAG, Instruct fine-tuning, Quantization, etc. - Predictive modelling experience in Python (Time Series/ Multivariable/ Causal) - Experience applying various machine learning techniques and understanding the key parameters that affect their performance - Experience of building systems that capture and utilize large data sets to quantify performance via metrics or KPIs - Excellent verbal and written communication - Comfortable working in a dynamic, fast-paced, innovative environment with several ongoing concurrent projects. Roles & Responsibilities: - Lead a team of Data Engineers, Analysts and Data scientists to carry out following activities: - Connect with internal / external POC to understand the business requirements - Coordinate with right POC to gather all relevant data artifacts, anecdotes, and hypothesis - Create project plan and sprints for milestones / deliverables - Spin VM, create and optimize clusters for Data Science workflows - Create data pipelines to ingest data effectively - Assure the quality of data with proactive checks and resolve the gaps - Carry out EDA, Feature Engineering & Define performance metrics prior to run relevant ML/DL algorithms - Research whether similar solutions have been already developed before building ML models - Create optimized data models to query relevant data efficiently - Run relevant ML / DL algorithms for business goal seek - Optimize and validate these ML / DL models to scale - Create light applications, simulators, and scenario builders to help business consume the end outputs - Create test cases and test the codes pre-production for possible bugs and resolve these bugs proactively - Integrate and operationalize the models in client ecosystem - Document project artifacts and log failures and exceptions. - Measure, articulate impact of DS projects on business metrics and finetune the workflow based on feedbacks Required Skills g, Pyspark, Python, SQL, GenAi, NLP, Casical ML, DL, Supervised ML, Transformer Models, Transformers, Unsupervised ML

Job Description: Graduate degree in a quantitative field (CS, statistics, applied mathematics, machine learning, or related discipline) Good programming skills in Python with strong working knowledge of Python's numerical, data analysis, or AI frameworks such as NumPy, Pandas, Scikit-learn, etc. Experience with LMs (Llama (1/2/3), T5, Falcon, Langchain or framework similar like Langchain) Candidate must be aware of entire evolution history of NLP (Traditional Language Models to Modern Large Language Models), training data creation, training set-up and finetuning Candidate must be comfortable interpreting research papers and architecture diagrams of Language Models Candidate must be comfortable with LORA, RAG, Instruct fine-tuning, Quantization, etc. Predictive modelling experience in Python (Time Series/ Multivariable/ Causal) Experience applying various machine learning techniques and understanding the key parameters that affect their performance Experience of building systems that capture and utilize large data sets to quantify performance via metrics or KPIs Excellent verbal and written communication Comfortable working in a dynamic, fast-paced, innovative environment with several ongoing concurrent projects. Roles & Responsibilities: Lead a team of Data Engineers, Analysts and Data scientists to carry out following activities: Connect with internal / external POC to understand the business requirements Coordinate with right POC to gather all relevant data artifacts, anecdotes, and hypothesis Create project plan and sprints for milestones / deliverables Spin VM, create and optimize clusters for Data Science workflows Create data pipelines to ingest data effectively Assure the quality of data with proactive checks and resolve the gaps Carry out EDA, Feature Engineering & Define performance metrics prior to run relevant ML/DL algorithms Research whether similar solutions have been already developed before building ML models Create optimized data models to query relevant data efficiently Run relevant ML / DL algorithms for business goal seek Optimize and validate these ML / DL models to scale Create light applications, simulators, and scenario builders to help business consume the end outputs Create test cases and test the codes pre-production for possible bugs and resolve these bugs proactively Integrate and operationalize the models in client ecosystem Document project artifacts and log failures and exceptions. Measure, articulate impact of DS projects on business metrics and finetune the workflow based on feedbacks Required Skills g, Pyspark, Python, SQL, Supervised ML, Transformer Models, Transformers, Unsupervised ML, GenAi

Dear Candidate, We are hiring an IoT Engineer to build and deploy connected devices and edge computing solutions. Ideal for engineers with a strong background in hardware-software integration. Key Responsibilities: Design and develop IoT device software and cloud integrations Implement communication protocols (MQTT, CoAP, BLE) Ensure security, performance, and scalability of IoT ecosystems Work with sensors, gateways, and embedded platforms Required Skills & Qualifications: Proficiency in C/C++, Python, or JavaScript Experience with IoT platforms (AWS IoT, Azure IoT Hub, Google IoT Core) Familiarity with edge computing and real-time systems Bonus: Knowledge of LPWAN, Zigbee, or industrial IoT (IIoT) Soft Skills: Strong troubleshooting and problem-solving skills. Ability to work independently and in a team. Excellent communication and documentation skills. Note: If interested, please share your updated resume and preferred time for a discussion. If shortlisted, our HR team will contact you. Kandi Srinivasa Reddy Delivery Manager Integra Technologies

Job Summary: As the Embedded Engineering Manager, you will lead the development and delivery of embedded firmware and hardware solutions for our IoT products. You will work closely with cross-functional teams including hardware, cloud, mobile, and product management to drive high-quality, scalable, and secure embedded solutions from concept through production. Key Responsibilities: Lead a team of embedded software and firmware engineers through the full product lifecycle. Architect, design, and review embedded system software and firmware for IoT devices. Collaborate with hardware engineers to integrate and validate system-level performance. Ensure secure, scalable, and power-efficient designs in accordance with IoT best practices. Define project roadmaps, allocate resources, and manage timelines and deliverables. Drive best practices for coding standards, testing, CI/CD, and code reviews. Identify and mitigate technical risks and resolve complex engineering challenges. Work closely with QA to develop and implement test strategies (unit, integration, system). Stay abreast of industry trends and new technologies to influence design decisions. Mentor, coach, and grow the embedded engineering team. Qualifications: Required: Bachelors or master’s degree in electrical engineering, Computer Engineering, or related field. 8+ years of experience in embedded systems development with at least 2–3 years in a leadership or management role. Strong proficiency in C/C++, RTOS, and embedded Linux environments. Experience with microcontrollers (ARM Cortex-M, etc.) and SoC platforms. Familiarity with communication protocols: BLE, Wi-Fi, MQTT, Zigbee, LoRa, etc. Solid understanding of secure coding practices and embedded security (e.g., secure boot, encryption). Experience in IoT product development from prototype to production. Strong interpersonal and communication skills. Preferred: Experience working with cloud-connected devices (AWS IoT, Azure IoT, etc.). Knowledge of hardware debugging tools (oscilloscopes, logic analyzers, JTAG). Familiarity with regulatory compliance (e.g., FCC, CE) and certifications. Agile/Scrum experience and familiarity with tools like Jira, Git, Jenkins.

Skilled IoT Developer with experience in embedded systems, expertise in C/C++ programming and Arduino-based development and testing. You will work closely with our R&D teams to build and deploy IoT automation systems for petrol pumps.

Preferred candidate profile 1. LLM Basics : (Llama, Gemini ) : Understand the basics of generative AI and LLMs, such as key terminology, uses, potential issues, and primary frameworks. One should know what the data is trained on and any potential biases/issues that there may be with the data . Knowledge on know exactly how big LLMs can be, how computationally expensive training will be, and the differences between training LLMs and machine learning models. 1. Prompt Engineering : Knowledge on designing inputs for LLMs once theyre developed. 2. Prompt Engineering with OpenAI : As a leading figure in LLMs and generative AI, it’s important to know how to use prompt engineering specifically with OpenAI tools, as you’ll likely be using them at some point in your career. 3. Question-Answering :Question-answering (QA) LLMs are a type of large language model that has been trained specifically to answer questions. 4. Fine-Tuning : Knowledge on Fine-tuning to improve the performance of an LLM on a variety of tasks, including text generation, translation, summarization, and question-answering. Customize LLMs for specific applications, such as customer service chatbots or medical diagnosis systems. Awareness on supervised learning. This involves providing the LLM with a dataset of labelled data, where each data point is a pair of input and output.. 1. Lang Chain : To architect complex LLM pipelines by chaining multiple models together (Classification, text generation, code generation, etc.)`Agents` to interact with all these external systems to execute actions dictated by LLMs. 2. Parameter Efficiency/Tuning : LORA 3. RAG Building : Generative AI, mastering RAG building—short for Retrieval-Augmented Generation—is becoming increasingly crucial 4. ML OPS and in particular LLMOps : Large Language Model Operations, is the practice of managing and maintaining large language models (LLMs) in a production setting 5. TensorFlow i s like a versatile toolbox for creating intelligent programs that can learn and understand various concepts, including machine learning, deep learning, and data science

Role Purpose The purpose of this role is to design, test and maintain software programs for operating systems or applications which needs to be deployed at a client end and ensure its meet 100% quality assurance parameters Machine Learning & Deep Learning – Strong understanding of LLM architectures, transformers, and fine-tuning techniques. MLOps & DevOps – Experience with CI/CD pipelines, model deployment, and monitoring. Vector Databases – Knowledge of storing and retrieving embeddings efficiently. Prompt Engineering – Ability to craft effective prompts for optimal model responses. Retrieval-Augmented Generation (RAG) – Implementing techniques to enhance LLM outputs with external knowledge. Cloud Platforms – Familiarity with AWS, Azure, or GCP for scalable deployments. Containerization & Orchestration – Using Docker and Kubernetes for model deployment. Observability & Monitoring – Tracking model performance, latency, and drift. Security & Ethics – Ensuring responsible AI practices and data privacy. Programming Skills – Strong proficiency in Python, SQL, and API development. Knowledge of Open-Source LLMs – Familiarity with models like LLaMA, Falcon, and Mistral. Fine-Tuning & Optimization – Experience with LoRA, quantization, and efficient training techniques. LLM Frameworks – Hands-on experience with Hugging Face, LangChain, or OpenAI APIs. Data Engineering – Understanding of ETL pipelines and data preprocessing. Microservices Architecture – Ability to design scalable AI-powered applications. Explainability & Interpretability – Techniques for understanding and debugging LLM outputs. Graph Databases – Knowledge of Neo4j or similar technologies for complex data relationships. Collaboration & Communication – Ability to work with cross-functional teams and explain technical concepts clearly. Deliver No. Performance Parameter Measure 1. Continuous Integration, Deployment & Monitoring of Software 100% error free on boarding & implementation, throughput %, Adherence to the schedule/ release plan 2. Quality & CSAT On-Time Delivery, Manage software, Troubleshoot queries, Customer experience, completion of assigned certifications for skill upgradation 3. MIS & Reporting 100% on time MIS & report generation Mandatory Skills: LLM Ops. Experience3-5 Years.

Senior Technical Architect Machine Learning Solutions We are looking for a Senior Technical Architect with deep expertise in Machine Learning (ML), Artificial Intelligence (AI) , and scalable ML system design . This role will focus on leading the end-to-end architecture of advanced ML-driven platforms, delivering impactful, production-grade AI solutions across the enterprise. Key Responsibilities Lead the architecture and design of enterprise-grade ML platforms , including data pipelines, model training pipelines, model inference services, and monitoring frameworks. Architect and optimize ML lifecycle management systems (MLOps) to support scalable, reproducible, and secure deployment of ML models in production. Design and implement retrieval-augmented generation (RAG) systems, vector databases , semantic search , and LLM orchestration frameworks (e.g., LangChain, Autogen). Define and enforce best practices in model development, versioning, CI/CD pipelines , model drift detection, retraining, and rollback mechanisms. Build robust pipelines for data ingestion, preprocessing, feature engineering , and model training at scale , using batch and real-time streaming architectures. Architect multi-modal ML solutions involving NLP, computer vision, time-series, or structured data use cases. Collaborate with data scientists, ML engineers, DevOps, and product teams to convert research prototypes into scalable production services . Implement observability for ML models including custom metrics, performance monitoring, and explainability (XAI) tooling. Evaluate and integrate third-party LLMs (e.g., OpenAI, Claude, Cohere) or open-source models (e.g., LLaMA, Mistral) as part of intelligent application design. Create architectural blueprints and reference implementations for LLM APIs, model hosting, fine-tuning, and embedding pipelines . Guide the selection of compute frameworks (GPUs, TPUs), model serving frameworks (e.g., TorchServe, Triton, BentoML) , and scalable inference strategies (batch, real-time, streaming). Drive AI governance and responsible AI practices including auditability, compliance, bias mitigation, and data protection. Stay up to date on the latest developments in ML frameworks, foundation models, model compression, distillation, and efficient inference . 14. Ability to coach and lead technical teams , fostering growth, knowledge sharing, and technical excellence in AI/ML domains. Experience managing the technical roadmap for AI-powered products , documentations ensuring timely delivery, performance optimization, and stakeholder alignment. Required Qualifications Bachelors or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 8+ years of experience in software architecture , with 5+ years focused specifically on machine learning systems and 2 years in leading team. Proven expertise in designing and deploying ML systems at scale , across cloud and hybrid environments. Strong hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face, Scikit-learn). Experience with vector databases (e.g., FAISS, Pinecone, Weaviate, Qdrant) and embedding models (e.g., SBERT, OpenAI, Cohere). Demonstrated proficiency in MLOps tools and platforms : MLflow, Kubeflow, SageMaker, Vertex AI, DataBricks, Airflow, etc. In-depth knowledge of cloud AI/ML services on AWS, Azure, or GCP – including certification(s) in one or more platforms. Experience with containerization and orchestration (Docker, Kubernetes) for model packaging and deployment. Ability to design LLM-based systems , including hybrid models (open-source + proprietary), fine-tuning strategies, and prompt engineering. Solid understanding of security, compliance , and AI risk management in ML deployments. Preferred Skills Experience with AutoML , hyperparameter tuning, model selection, and experiment tracking. Knowledge of LLM tuning techniques : LoRA, PEFT, quantization, distillation, and RLHF. Knowledge of privacy-preserving ML techniques , federated learning, and homomorphic encryption Familiarity with zero-shot, few-shot learning , and retrieval-enhanced inference pipelines. Contributions to open-source ML tools or libraries. Experience deploying AI copilots, agents, or assistants using orchestration frameworks.