Job Description

Title:

Technical Lead (AI/ML/GenAI)

:

We are seeking a highly skilled and innovative Technical Lead ( AI/ML/GenAI) with strong hands-on experience in Agentic AI, Machine Learning, and Cloud Platforms like AWS and Databricks. The ideal candidate will be proficient in building intelligent systems using agentic frameworks to deliver scalable, production-grade solutions such as chatbots and autonomous agents.

Key Responsibilities:

Lead the design, development, and deployment of advanced machine learning models and algorithms for various applications. Build and optimize chatbots and autonomous agents using LLM endpoints and frameworks like LangChain, Semantic Kernel, or similar. Implement vector search using technologies such as FAISS, Weaviate, Pinecone, or Milvus for semantic retrieval and RAG (Retrieval-Augmented Generation). Collaborate with data engineers and product teams to integrate ML models into production systems. Monitor and maintain deployed models, ensuring performance, scalability, and reliability. Conduct experiments, A/B testing, and model evaluations to improve system accuracy and efficiency. Stay updated with the latest advancements in AI/ML, especially in agentic systems and generative AI. Ensure robust security, compliance, and governance, including role-based access control, audit logging, and data privacy controls. Collaborate with data scientists, ML engineers, and product teams to deliver scalable, production-grade GenAI solutions. Participate in code reviews, architecture discussions, and continuous improvement of the GenAI platform.

Required Skills & Qualifications

8+ years of experience in

Machine Learning, Deep Learning, and AI system design

. Strong hands-on experience

with Agentic AI frameworks and LLM APIs (e.g., OpenAI)

Certifications in AI/ML or cloud-based AI platforms (

AWS, GCP, Azure

). Proficiency in Python and ML libraries like scikit-learn, XGBoost, etc. Experience with AWS services such as SageMaker, Lambda, S3, EC2, and IAM. Expertise in Databricks for collaborative data science and ML workflows. Solid understanding of vector databases and semantic search. Hands-on experience with MLOps including containerization (Docker, Kubernetes), CI/CD, and model monitoring along with tools like MLflow, Experience with RAG pipelines & LangChain. LLM orchestration, or agentic frameworks. Knowledge of data privacy Exposure to real-time inference systems and streaming data. Experience in regulated industries such as healthcare, biopharma Proven ability to scale AI teams and lead complex AI projects in high-growth environments Oil & Gas, refinery operations & financial services exposure is preferred * Master's/ bachelor's degree (or equivalent) in computer science, mathematics, or related field