Description :
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 privacyExposure to real-time inference systems and streaming data.Experience in regulated industries such as healthcare, biopharmaProven ability to scale AI teams and lead complex AI projects in high-growth environmentsOil & Gas, refinery operations & financial services exposure is preferredMasters / bachelors degree (or equivalent) in computer science, mathematics, or related field(ref : hirist.tech)