AI/ML Technical Specialist

· We are looking for a Senior AI/ML Engineer with strong hands-on experience across Machine Learning, Deep Learning, MLOps, Generative AI, and Agentic AI.

· The candidate will be responsible for designing, developing, deploying, and scaling AI solutions for enterprise use cases.

Key Responsibilities

· Design and develop end-to-end Machine Learning and Deep Learning solutions.

· Build and productionize ML models using appropriate algorithms and frameworks.

· Develop and manage MLOps pipelines covering model training, deployment, monitoring, and lifecycle management.

· Build Generative AI applications using LLMs, prompt engineering, embeddings, RAG, and vector databases.

· Design and implement Agentic AI solutions, including AI agents, tool calling, multi-agent workflows, and orchestration.

· Integrate AI solutions with enterprise APIs, databases, applications, and knowledge sources.

· Deploy scalable AI solutions on AWS, Azure, or Google Cloud Platform.

· Implement AI application monitoring, evaluation, observability, security, and responsible AI practices.

· Collaborate with product, engineering, data, and business teams to translate business problems into AI solutions.

· Mentor junior engineers and contribute to AI architecture and technology strategy.

Skills: AI/ML Engineer Strong Python, LLM and Prompt engineering expertise with Java, · experience in AI/ML, Data Science, Machine Learning Engineering, or a related field., Core Technical Skills

· Programming: Python, SQL

· Machine Learning: Scikit-learn, XGBoost/LightGBM

· Deep Learning: PyTorch / TensorFlow

· GenAI: LLMs, Prompt Engineering, Embeddings, RAG

· Agentic AI: LangGraph, LangChain or equivalent frameworks

· Vector Search: OpenSearch, FAISS, Pinecone or equivalent

· MLOps: MLflow, Docker, CI/CD, model deployment & monitoring

· Cloud: AWS / Azure / Google Cloud Platform

· APIs & Engineering: REST APIs, FastAPI, Git, containerization

Good to Have

· MCP and A2A protocols

· Multi-agent systems

· LLM and RAG evaluation

· AI observability and guardrails

· Kubernetes

· Knowledge Graph / Graph RAG

· Experience with enterprise-scale AI applications

Candidate Profile

· The ideal candidate should be a hands-on AI engineer who can take an AI use case from problem definition through development, deployment, and production monitoring.

· Strong problem-solving, communication, system-design, and mentoring skills are expected.