17 Pinecone Jobs

We are seeking a highly skilled and innovative Data Scientist with a strong focus on Generative AI, NLP, and Large Language Models (LLMs). The ideal candidate will design, develop, and deploy end-to-end data science solutions that harness the power of advanced ML, deep learning, and Gen AI technologies to drive real-world impact. Key Responsibilities: Design and implement scalable data science solutions using Generative AI, NLP, and ML techniques. Develop, fine-tune, and evaluate Large Language Models (LLMs) such as GPT, BERT, and similar architectures. Analyze structured and unstructured data to generate actionable insights for business problems. Collaborate cross-functionally with engineering, product, and business teams to integrate AI models into production systems. Conduct cutting-edge research in Gen AI and deep learning; evaluate and apply recent advancements. Build and maintain robust pipelines for model training, evaluation, monitoring, and deployment. Communicate complex technical findings clearly to technical and non-technical stakeholders. Required Skills & Qualifications: Strong hands-on experience with Generative AI, LLMs (e.g., OpenAI, Hugging Face Transformers, etc.). Proven expertise in core NLP tasks: text classification, summarization, named entity recognition, sentiment analysis, etc. Proficient in Python and related libraries: NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch. Experience developing, validating, and deploying ML models in production environments. Familiarity with vector databases (e.g., FAISS, Pinecone), embeddings, and semantic search. Exposure to cloud platforms like AWS, Azure, or GCP, and MLOps tools/workflows (CI/CD, model monitoring, etc.). Preferred Qualifications: Experience in prompt engineering, Retrieval-Augmented Generation (RAG), or fine-tuning/customizing LLMs. Contributions to AI research papers, open-source projects, or participation in ML competitions (e.g., Kaggle) is a plus. Knowledge of responsible AI practices, model interpretability, and bias mitigation techniques.

Role Senior Developer Experience:7 to 8 years. Skills Good to have GenAI experience of 1-2 years 1.Python with experience in AI/ML libraries such as TensorFlow, Pytorch, NumPy, pypdf 2. GenAI Skills - RAG, Prompt Engineering, Vector DB (Pinecone, Weaviate) 3. Familiarity with AI/ML workloads in Azure/Amazon

Role Senior Developer Experience:7 to 8 years. Skills Good to have GenAI experience of 1-2 years 1.Python with experience in AI/ML libraries such as TensorFlow, Pytorch, NumPy, pypdf 2. GenAI Skills - RAG, Prompt Engineering, Vector DB (Pinecone, Weaviate) 3. Familiarity with AI/ML workloads in Azure/Amazon

Role Senior Developer Experience:7 to 8 years. Skills Good to have GenAI experience of 1-2 years 1.Python with experience in AI/ML libraries such as TensorFlow, Pytorch, NumPy, pypdf 2. GenAI Skills - RAG, Prompt Engineering, Vector DB (Pinecone, Weaviate) 3. Familiarity with AI/ML workloads in Azure/Amazon

DevOps, Cloud Infrastructure and CI/CD Strong hands-on experience with AWS services Exposure to AI pipelines, especially speech-to-text and vector databases (e.g., Pinecone) Knowledge of PostgreSQL performance tuning and replication 9220166817 tanya

Dear Candidate, A Warm Greeting for SAIS IT Services! We are hiring for of product development for our client. Interested people can share your CV to Jyoti.r@saisservices.com, For more queries, kindly reach me on 8360298749 with the below mentioned details; Please fill the below details: Total Exp- CTC- ECTC- Notice Period- Current Location- Comfortable for Work from Office- Job Description: Were Hiring: of product development Location: Remote Job Title: of product development Experience 2+ years Location Hyderabad Work mode: Remote Notice Period: immediate -15 Days JOB DESCRIPTION SENIOR FULL STACK DEVELOPER Required - Using vector stores (e.g., Pinecone, ChromaDB) and embedding models. Role specifics Be a part of the core scrum team involved in the development of the flagship health care suite Ready to collaborate with cross-functional teams to define, design, and ship new features comprising of creating new interfaces and REST APIs and integrations Unit-test code for robustness, including edge cases, usability, and general reliability Implement AI workflows including prompt engineering, vector databases, embedding models, and fine-tuning where applicable. Optimize applications for performance, scalability, and security. Implement user stories through high quality code Passionate about technology, self-motivated, and eager to continue learning as well as collaborate with others Participate in bug fixes and grooming sessions Regards, Jyoti Rani 8360298749 Jyoti.r@saisservices.com

Key Responsibilities Design, develop, and deploy AI solutions that span both traditional ML models and GenAI-based systems . Build machine learning pipelines using algorithms like linear/logistic regression, decision trees, SVMs, random forests, XGBoost , clustering (K-means, DBSCAN), and time series forecasting . Analyze datasets to derive meaningful insights and build predictive models to solve business problems. Work on GenAI applications using LLMs, including prompt engineering, fine-tuning, and retrieval-augmented generation (RAG). Develop and integrate LLM-based features using frameworks like LangChain, Hugging Face Transformers, or OpenAI API. Collaborate with data, product, and engineering teams to define and implement AI-driven functionalities . Apply statistical modeling and inference techniques for feature selection, model evaluation, and data exploration. Optimize performance of ML and GenAI models through hyperparameter tuning, cross-validation , and error analysis. Design and maintain data pipelines and ML workflows using tools like Airflow, DVC , or MLflow . Deploy models into production with appropriate MLOps practices, ensuring monitoring, retraining , and version control . Research and evaluate advancements in both traditional ML and LLM-based AI . Skills & Experience 3-6 years of experience in AI/ML using Python. Proficient in machine learning algorithms (classification, regression, clustering, dimensionality reduction, ensemble methods). Hands-on experience with GenAI/LLM applications (prompt design, RAG, fine-tuning, etc.). Familiarity with data preprocessing, feature engineering , and working with structured and unstructured data . Proficient in Python ML libraries : Scikit-learn, XGBoost, LightGBM, Pandas, NumPy. Experience with deep learning frameworks : PyTorch or TensorFlow. Familiarity with vector databases (e.g., FAISS, Pinecone) and LLM orchestration tools (LangChain, Hugging Face). Experience in model evaluation techniques , including AUC-ROC, precision-recall, RMSE, etc. Familiarity with cloud AI services (AWS SageMaker, GCP AI Platform, or Azure ML). Solid understanding of MLOps tools : MLflow, Docker, Git, CI/CD pipelines. Strong analytical, communication, and collaboration skills.

Your day at NTT DATA We are seeking an experienced Data Engineer to join our team in delivering cutting-edge Generative AI (GenAI) solutions to clients. The successful candidate will be responsible for designing, developing, and deploying data pipelines and architectures that support the training, fine-tuning, and deployment of LLMs for various industries. This role requires strong technical expertise in data engineering, problem-solving skills, and the ability to work effectively with clients and internal teams. What youll be doing Key Responsibilities: Design, develop, and manage data pipelines and architectures to support GenAI model training, fine-tuning, and deployment Data Ingestion and Integration: Develop data ingestion frameworks to collect data from various sources, transform, and integrate it into a unified data platform for GenAI model training and deployment. GenAI Model Integration: Collaborate with data scientists to integrate GenAI models into production-ready applications, ensuring seamless model deployment, monitoring, and maintenance. Cloud Infrastructure Management: Design, implement, and manage cloud-based data infrastructure (e.g., AWS, GCP, Azure) to support large-scale GenAI workloads, ensuring cost-effectiveness, security, and compliance. Write scalable, readable, and maintainable code using object-oriented programming concepts in languages like Python, and utilize libraries like Hugging Face Transformers, PyTorch, or TensorFlow Performance Optimization: Optimize data pipelines, GenAI model performance, and infrastructure for scalability, efficiency, and cost-effectiveness. Data Security and Compliance: Ensure data security, privacy, and compliance with regulatory requirements (e.g., GDPR, HIPAA) across data pipelines and GenAI applications. Client Collaboration: Collaborate with clients to understand their GenAI needs, design solutions, and deliver high-quality data engineering services. Innovation and R&D: Stay up to date with the latest GenAI trends, technologies, and innovations, applying research and development skills to improve data engineering services. Knowledge Sharing: Share knowledge, best practices, and expertise with team members, contributing to the growth and development of the team. Bachelors degree in computer science, Engineering, or related fields (Masters recommended) Experience with vector databases (e.g., Pinecone, Weaviate, Faiss, Annoy) for efficient similarity search and storage of dense vectors in GenAI applications 5+ years of experience in data engineering, with a strong emphasis on cloud environments (AWS, GCP, Azure, or Cloud Native platforms) Proficiency in programming languages like SQL, Python, and PySpark Strong data architecture, data modeling, and data governance skills Experience with Big Data Platforms (Hadoop, Databricks, Hive, Kafka, Apache Iceberg), Data Warehouses (Teradata, Snowflake, BigQuery), and lakehouses (Delta Lake, Apache Hudi) Knowledge of DevOps practices, including Git workflows and CI/CD pipelines (Azure DevOps, Jenkins, GitHub Actions) Experience with GenAI frameworks and tools (e.g., TensorFlow, PyTorch, Keras) Nice to have: Experience with containerization and orchestration tools like Docker and Kubernetes Integrate vector databases and implement similarity search techniques, with a focus on GraphRAG is a plus Familiarity with API gateway and service mesh architectures Experience with low latency/streaming, batch, and micro-batch processing Familiarity with Linux-based operating systems and REST APIs

Job Description: We are seeking a highly skilled and passionate AI/ML Engineer with strong expertise in Generative AI and Large Language Models (LLMs) . The ideal candidate will have hands-on experience in building, fine-tuning, and deploying agentic AI systems using modern GenAI frameworks. You will work on cutting-edge projects involving prompt engineering , RAG pipelines , and memory architectures such as vector databases. Responsibilities: Design and implement AI/ML solutions using modern LLM architectures and agentic AI concepts . Build and optimize intelligent agents using frameworks such as LangChain, AutoGen, CrewAI , or Semantic Kernel . Develop and fine-tune generative AI models with Transformers , HuggingFace , OpenAI API , etc. Implement and enhance Retrieval-Augmented Generation (RAG) pipelines and memory systems like vector databases (e.g., FAISS, Pinecone). Write high-performance Python code to support experimentation, model integration, and API interactions. Collaborate cross-functionally with product, design, and engineering teams in an agile development environment. Deploy AI solutions on cloud platforms (AWS, Azure, or GCP) with a focus on scalability and performance. Stay updated with the latest advancements in the AI/ML/GenAI space. Required Experience: 3 to 8 years of experience in AI/ML , with at least 1 year in Generative AI / LLM-based projects . Proven expertise in Python programming and related libraries for ML/GenAI. Hands-on experience with one or more GenAI frameworks (LangChain, AutoGen, etc.). Solid understanding of prompt engineering , RAG , vector DBs , and agent-based systems . Cloud deployment experience (AWS, Azure, or GCP) is a must. Strong analytical and problem-solving skills.

"We're Hiring For Generative Ai Engineer Role at Bangalore Location" Position: Generative Ai Engineer Experience: 7+ Years Location: Bangalore Responsibilities Develop and deploy scalable big data and AI solutions using Databricks and Azure. Implement RAG pipelines and integrate GenAI APIs for document and image retrieval. Perform finetuning of LLM models and manage prompt engineering workflows. Ensure the end-to-end solution is optimized for performance and scalability. Collaborate with software and ML teams for integration of AI capabilities. Deploy GenAI projects in production in Azure and Databricks. (Must) Required Qualifications Bachelors or Masters degree in Computer Science or related field. 7+ years of experience in big data and AI development. Strong experience with Databricks, Pyspark, and Python. Experience in deploying GenAI projects in production. Preferred Qualifications Familiarity with RAG architecture and document-based retrieval systems. Experience with Azure OpenAI, LangChain, Pinecone or similar tools. Experience with deploying LLM, VLM Models in Cloud Experience with MLOps tools such as MLFlow or similar tools. Technologies Used Python, Pyspark, Databricks, Azure ML, LangChain, OpenAI API, Pinecone, MLFlow More information: +91 73597 10155 | rushit@tekpillar.com

Job Title: ML Prompt Engineer Location - Bangalore Hybrid . Job Description: Principle Developer - ML/Prompt Engineer Technologies: Amazon Bedrock, RAG Models, Java, Python, C or C++, AWS Lambda Responsibilities: Responsible for developing, deploying, and maintaining a Retrieval Augmented Generation (RAG) model in Amazon Bedrock, our cloud-based platform for building and scaling generative AI applications. Design and implement a RAG model that can generate natural language responses, commands, and actions based on user queries and context, using the Anthropic Claude model as the backbone. Integrate the RAG model with Amazon Bedrock, our platform that offers a choice of high-performing foundation models from leading AI companies and Amazon via a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. Optimize the RAG model for performance, scalability, and reliability, using best practices and robust engineering methodologies. Design, test, and optimize prompts to improve performance, accuracy, and alignment of large language models across diverse use cases. Develop and maintain reusable prompt templates, chains, and libraries to support scalable and consistent GenAI applications. Skills/Qualifications: Experience in programming with at least one software language, such as Java, Python, or C/C++. Experience in working with generative AI tools, models, and frameworks, such as Anthropic, OpenAI, Hugging Face, TensorFlow, PyTorch, or Jupyter. Experience in working with RAG models or similar architectures, such as RAG, Ragna, or Pinecone. Experience in working with Amazon Bedrock or similar platforms, such as AWS Lambda, Amazon SageMaker, or Amazon Comprehend. Ability to design, iterate, and optimize prompts for various LLM use cases (e.g., summarization, classification, translation, Q&A, and agent workflows). Deep understanding of prompt engineering techniques (zero-shot, few-shot, chain-of-thought, etc.) and their effect on model behavior. Familiarity with prompt evaluation strategies, including manual review, automatic metrics, and A/B testing frameworks. Experience building prompt libraries, reusable templates, and structured prompt workflows for scalable GenAI applications. Ability to debug and refine prompts to improve accuracy, safety, and alignment with business objectives. Awareness of prompt injection risks and experience implementing mitigation strategies. Familiarity with prompt tuning, parameter-efficient fine-tuning (PEFT), and prompt chaining methods. Familiarity with continuous deployment and DevOps tools preferred. Experience with Git preferred Experience working in agile/scrum environments Successful track record interfacing and communicating effectively across cross-functional teams. Good communication, analytical and presentation skills, problem-solving skills and learning attitude

Job Title: AI/ML and Chatbot Lead Experience Level: 10+ Years (Lead/Architect level) Location: Remote Employment Type: Full-time No. of Positions: 1 Job Overview: We are seeking a visionary and hands-on AI/ML and Chatbot Lead to spearhead the design, development, and deployment of enterprise-wide Conversational and Generative AI solutions. This role will establish and scale our AI Lab function, define chatbot and multimodal AI strategies, and deliver intelligent automation solutions that enhance user engagement and operational efficiency. Key Responsibilities Define and lead the enterprise-wide strategy for Conversational AI, Multimodal AI, and Large Language Models (LLMs). Build an AI/Chatbot Lab , creating a roadmap and driving innovations across in-app, generative, and conversational AI. Architect scalable AI/ML systems including presentation, orchestration, AI, and data layers. Collaborate with business stakeholders to assess needs, conduct ROI analyses, and deliver impactful AI use cases. Identify and implement agentic AI capabilities and SaaS optimization opportunities. Deliver POCs, pilots, and MVPs owning the design, development, and deployment lifecycle. Lead, mentor, and scale a high-performing team of AI/ML engineers and chatbot developers . Build multi-turn, memory-aware conversations using frameworks like LangChain or Semantic Kernel . Integrate bots with platforms like Salesforce, NetSuite, Slack , and custom applications via APIs/webhooks. Implement and monitor chatbot KPIs using tools like Kibana , Grafana , and custom dashboards. Champion ethical AI , governance, and data privacy/security best practices. Must-Have Skills 10+ years in AI/ML; demonstrable success in chatbot, conversational AI , and generative AI implementations. Experience building and operationalizing an AI/Chatbot architecture framework used enterprise-wide. Expertise in: Python , LangChain, ElasticSearch, NLP (spaCy, NLTK, Hugging Face) LLMs (e.g., GPT, BERT), RAG, prompt engineering Chatbot platforms (Azure OpenAI, MS Bot Framework), CLU, CQA AI solution deployment and monitoring at scale Familiarity with: Machine learning algorithms, deep learning, reinforcement learning NLP techniques for NLU/NLG Cloud platforms ( AWS, Azure, GCP ), Docker , Kubernetes Vector DBs (Pinecone, Weaviate, Qdrant) Semantic search, knowledge graphs, intelligent document processing Strong grasp of AI governance , documentation, and compliance standards Excellent team leadership, communication, and documentation skills Good-to-Have Skills Experience with Glean , Perplexity.ai , Rasa , XGBoost Familiarity with Salesforce , NetSuite , and business domains like Customer Success Knowledge of RPA tools like UiPath and its AI Center Role & responsibilities Interested candidate can call at 7087707007

We're looking for an experienced AI/ML Technical Lead to architect and drive the development of our intelligent conversation engine. Youll lead model selection, integration, training workflows (RAG/fine-tuning), and scalable deployment of natural language and voice AI components. This is a foundational hire for a technically ambitious platform. Key Responsibilities AI System Architecture: Design the architecture of the AI-powered agent including LLM-based conversation workflows, voice bots, and follow-up orchestration. Model Integration & Prompt Engineering: Leverage APIs from OpenAI, Anthropic, or deploy open models (e.g., LLaMA 3, Mistral). Implement effective prompt strategies and retrieval-augmented generation (RAG) pipelines for contextual responses. Data Pipelines & Knowledge Management: Build secure data pipelines to ingest, embed, and serve tenant-specific knowledge bases (FAQs, scripts, product docs) using vector databases (e.g., Pinecone, Weaviate). Voice & Text Interfaces: Implement and optimize multimodal agents (text + voice) using ASR (e.g., Whisper), TTS (e.g., Polly), and NLP for automated qualification and call handling. Conversational Flow Orchestration: Design dynamic, stateful conversations that can take actions (e.g., book meetings, update CRM records) using tools like LangChain, Temporal, or n8n. Platform Scalability: Ensure models and agent workflows scale across tenants with strong data isolation, caching, and secure API access. Lead a Cross-Functional Team: Collaborate with backend, frontend, and DevOps engineers to ship intelligent, production-ready features. Monitoring & Feedback Loops: Define and monitor conversation analytics (drop-offs, booking rates, escalation triggers), and create pipelines to improve AI quality continuously. Qualifications Must-Haves: 5+ years of experience in ML/AI, with at least 2 years leading conversational AI or LLM projects. Strong background in NLP, dialog systems, or voice AI preferably with production experience. Experience with OpenAI, or open-source LLMs (e.g. LLaMA, Mistral, Falcon) and orchestration tools (LangChain, etc.). Proficiency with Python and ML frameworks (Hugging Face, PyTorch, TensorFlow). Experience deploying RAG pipelines, vector DBs (e.g. Pinecone, Weaviate), and managing LLM-agent logic. Familiarity with voice processing (ASR, TTS, IVR design). Solid understanding of API-based integration and microservices. Deep care for data privacy, multi-tenancy security, and ethical AI practices. Nice-to-Haves: Experience with CRM ecosystems (e.g. Salesforce, HubSpot) and how AI agents sync actions to CRMs. Knowledge of sales pipelines and marketing automation tools. Exposure to calendar integrations (Google Calendar API, Microsoft Graph). Knowledge of Twilio APIs (SMS, Voice, WhatsApp) and channel orchestration logic. Familiarity with Docker, Kubernetes, CI/CD, and scalable cloud infrastructure (AWS/GCP/Azure). What We Offer Founding team role with strong ownership and autonomy Opportunity to shape the future of AI-powered sales Flexible work environment Competitive salary Access to cutting-edge AI tools and training resources Post your resume and any relevant project links (GitHub, blog, portfolio) to career@sourcedeskglobal.com. Include a short note on your most interesting AI project or voicebot/conversational AI experience.

Job Type: Full Time Job Description: We are seeking an experienced AI Engineer with 4-5 years of hands-on experience in designing and implementing AI solutions. The ideal candidate should have a strong foundation in developing AI/ML-based solutions, including expertise in Computer Vision (OpenCV). Additionally, proficiency in developing, fine-tuning, and deploying Large Language Models (LLMs) is essential. As an AI Engineer, candidate will work on cutting-edge AI applications, using LLMs like GPT, LLaMA, or custom fine-tuned models to build intelligent, scalable, and impactful solutions. candidate will collaborate closely with Product, Data Science, and Engineering teams to define, develop, and optimize AI/ML models for real-world business applications. Key Responsibilities: Research, design, and develop AI/ML solutions for real-world business applications, RAG is must. Collaborate with Product & Data Science teams to define core AI/ML platform features. Analyze business requirements and identify pre-trained models that align with use cases. Work with multi-agent AI frameworks like LangChain, LangGraph, and LlamaIndex. Train and fine-tune LLMs (GPT, LLaMA, Gemini, etc.) for domain-specific tasks. Implement Retrieval-Augmented Generation (RAG) workflows and optimize LLM inference. Develop NLP-based GenAI applications, including chatbots, document automation, and AI agents. Preprocess, clean, and analyze large datasets to train and improve AI models. Optimize LLM inference speed, memory efficiency, and resource utilization. Deploy AI models in cloud environments (AWS, Azure, GCP) or on-premises infrastructure. Develop APIs, pipelines, and frameworks for integrating AI solutions into products. Conduct performance evaluations and fine-tune models for accuracy, latency, and scalability. Stay updated with advancements in AI, ML, and GenAI technologies. Required Skills & Experience: AI & Machine Learning: Strong experience in developing & deploying AI/ML models. Generative AI & LLMs: Expertise in LLM pretraining, fine-tuning, and optimization. NLP & Computer Vision: Hands-on experience in NLP, Transformers, OpenCV, YOLO, R-CNN. AI Agents & Multi-Agent Frameworks: Experience with LangChain, LangGraph, LlamaIndex. Deep Learning & Frameworks: Proficiency in TensorFlow, PyTorch, Keras. Cloud & Infrastructure: Strong knowledge of AWS, Azure, or GCP for AI deployment. Model Optimization: Experience in LLM inference optimization for speed & memory efficiency. Programming & Development: Proficiency in Python and experience in API development. Statistical & ML Techniques: Knowledge of Regression, Classification, Clustering, SVMs, Decision Trees, Neural Networks. Debugging & Performance Tuning: Strong skills in unit testing, debugging, and model evaluation. Hands-on experience with Vector Databases (FAISS, ChromaDB, Weaviate, Pinecone). Good to Have: Experience with multi-modal AI (text, image, video, speech processing). Familiarity with containerization (Docker, Kubernetes) and model serving (FastAPI, Flask, Triton).

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.

About the job: The Red Hat, Experience Engineering (XE) team is looking for a skilled Python Developer with 2+ years of experience to join our Software Engineering team. In this role, the ideal candidate should have a strong background in Python development, a deep understanding of LLMs, and the ability to debug and optimize AI applications. Your work will directly impact our product development, helping us drive innovation and improve the customer experience. What will you do? Develop and maintain Python-based applications, integrating LLMs and AI-powered solutions. Collaborate with cross-functional teams (product managers, software engineers, and data teams) to understand requirements and translate them into data-driven solutions. Assist in the development, testing, and optimization of AI-driven features. Optimize performance and scalability of applications utilizing LLMs. Debug and resolve Python application errors, ensuring stability and efficiency. Conduct exploratory data analysis and data cleaning to prepare raw data for modelling. Optimize and maintain data storage and retrieval systems for model input/output. Research and experiment with new LLM advancements and AI tools to improve existing applications. Document workflows, model architectures, and code to ensure reproducibility and knowledge sharing across the team. What will you bring? Bachelor's degree in Computer Science, Software Engineering, or a related field with 2+ years of relevant experience. Strong proficiency in Python, including experience with frameworks like FastAPI/ Flask, or Django. Understanding of fundamental AI/ML concepts, algorithms, techniques and implementation of workflows. Familiarity with DevOps/MLOps practices and tools for managing the AI/ML lifecycle in production environments. Understanding of LLM training processes and data requirements. Experience in LLM fine-tuning, RAG and prompt engineering. Hands-on experience with LLMs (e.g., OpenAI GPT, Llama, or other transformer models) and their integration into applications(e.g. LangChain or Llama Stack). Familiarity with REST APIs, data structures, and algorithms. Strong problem-solving skills with the ability to analyze and debug complex issues. Experience with Git, CI/CD pipelines, and Agile methodologies. Experience working with cloud-based environments (AWS, GCP, or Azure) is a plus. Knowledge of vector databases (e.g., Pinecone, FAISS, ChromaDB) is a plus.

We are looking for an AI Engineer (Senior Software Engineer). Interested candidates email me resumes on mayura.joshi@lionbridge.com OR WhatsApp on 9987538863 Responsibilities: Design, develop, and optimize AI solutions using LLMs (e.g., GPT-4, LLaMA, Falcon) and RAG frameworks. Implement and fine-tune models to improve response relevance and contextual accuracy. Develop pipelines for data retrieval, indexing, and augmentation to improve knowledge grounding. Work with vector databases (e.g., Pinecone, FAISS, Weaviate) to enhance retrieval capabilities. Integrate AI models with enterprise applications and APIs. Optimize model inference for performance and scalability. Collaborate with data scientists, ML engineers, and software developers to align AI models with business objectives. Ensure ethical AI implementation, addressing bias, explainability, and data security. Stay updated with the latest advancements in generative AI, deep learning, and RAG techniques. Requirements: 8+ years experience in software development according to development standards. Strong experience in training and deploying LLMs using frameworks like Hugging Face Transformers, OpenAI API, or LangChain. Proficiency in Retrieval-Augmented Generation (RAG) techniques and vector search methodologies. Hands-on experience with vector databases such as FAISS, Pinecone, ChromaDB, or Weaviate. Solid understanding of NLP, deep learning, and transformer architectures. Proficiency in Python and ML libraries (TensorFlow, PyTorch, LangChain, etc.). Experience with cloud platforms (AWS, GCP, Azure) and MLOps workflows. Familiarity with containerization (Docker, Kubernetes) for scalable AI deployments. Strong problem-solving and debugging skills. Excellent communication and teamwork abilities Bachelors or Masters degree in computer science, AI, Machine Learning, or a related field.