8.0 - 12.0 years

0 Lacs

chennai, tamil nadu

On-site

The Senior Data Science Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities. The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation. Key Responsibilities: 1. Architecting & Scaling Agentic AI Solutions: - Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. - Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains. - Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. 2. Hands-On Development & Optimization: - Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. - Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. - Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making. 3. Driving AI Innovation & Research: - Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. - Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions. - Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops. 4. AI Strategy & Business Impact: - Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. - Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. 5. Mentorship & Capability Building: - Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures. - Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents.,

Posted 5 days ago

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Machine Learning Engineer Sunflower Lab

2.0 - 6.0 years

0 Lacs

vadodara, gujarat

On-site

As a Machine Learning Engineer, you will be responsible for designing and implementing scalable machine learning models throughout the entire lifecycle - from data preprocessing to deployment. Your role will involve leading feature engineering and model optimization efforts to enhance performance and accuracy. Additionally, you will build and manage end-to-end ML pipelines using MLOps practices, ensuring seamless deployment, monitoring, and maintenance of models in production environments. Collaboration with data scientists and product teams will be key in understanding business requirements and translating them into effective ML solutions. You will conduct advanced data analysis, create visualization dashboards for insights, and maintain detailed documentation of models, experiments, and workflows. Moreover, mentoring junior team members on best practices and technical skills will be part of your responsibilities to foster growth within the team. In terms of required skills, you must have at least 3 years of experience in machine learning development, with a focus on the end-to-end model lifecycle. Proficiency in Python using Pandas, NumPy, and Scikit-learn for advanced data handling and feature engineering is crucial. Strong hands-on expertise in TensorFlow or PyTorch for deep learning model development is also a must-have. Desirable skills include experience with MLOps tools like MLflow or Kubeflow for model management and deployment, familiarity with big data frameworks such as Spark or Dask, and exposure to cloud ML services like AWS SageMaker or GCP AI Platform. Additionally, working knowledge of Weights & Biases and DVC for experiment tracking and versioning, as well as experience with Ray or BentoML for distributed training and model serving, will be considered advantageous. Join our team and contribute to cutting-edge machine learning projects while continuously improving your skills and expertise in a collaborative and innovative environment.,

Posted 1 week ago

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