Associate Principal - Architecture RiverForest Connections Private Limited

8.0 - 12.0 years

0 Lacs

chennai, tamil nadu

On-site

You will be responsible for building and maintaining robust machine learning pipelines in a cloud-based environment, ensuring efficient model deployment, monitoring, and lifecycle management. Your expertise in MLOps, specifically with Google Cloud Platform (GCP) and Vertex AI, will be essential. You should have a deep understanding of model performance drift detection and GPU accelerators. Your main tasks will include building and maintaining scalable MLOps pipelines in GCP Vertex AI for end-to-end machine learning workflows, managing the full MLOps lifecycle from data preprocessing to model monitoring and drift detection. Real-time model monitoring and drift detection will be crucial to ensure optimal model performance over time. You will be responsible for building and executing CICD containerization and orchestration tools, with hands-on experience in Jenkins, GitHub Pipelines, Docker, Kubernetes, and OpenShift. Optimizing model training and inference processes using GPU accelerators and CUDA will also be part of your role. Collaborating with cross-functional teams to automate and streamline machine learning model deployment and monitoring will be essential. Utilizing Python 3.10 with libraries such as pandas, NumPy, and TensorFlow for data processing and model development is required. Setting up infrastructure for continuous training, testing, and deployment of machine learning models while ensuring scalability, security, and high availability in all machine learning operations by implementing best practices in MLOps will be key to success. Preferred Candidate's Profile: - Experience: 8.5-12 Years (Lead Role: 12 Years+) - Experience in MLOps, building ML pipelines, and experience in GCP Vertex AI - Deep understanding of the MLOps lifecycle and automation of ML workflows - Proficiency in Python 3.10 and related libraries such as pandas, NumPy, and TensorFlow - Strong experience in GPU accelerators and CUDA for model training and optimization - Proven experience in model monitoring, drift detection, and maintaining model accuracy over time - Familiarity with CICD pipelines, Docker, Kubernetes, and cloud infrastructure - Strong problem-solving skills with the ability to work in a fast-paced environment - Experience with tools like Evidently AI for model monitoring and drift detection - Knowledge of data versioning and model version control techniques - Familiarity with TensorFlow Extended (TFX) or other ML workflow orchestration frameworks - Excellent communication and collaboration skills with the ability to work cross-functionally across teams (Note: The above job description is a summary of the responsibilities and requirements for this position. It is not exhaustive and may be subject to change based on the needs of the organization.),

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Senior Data Science Lead Brillio

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.,

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