AI/ML Technical Specialist

Key Responsibilities

·                  experience in AI/ML, Data Science, Machine Learning Engineering, or a related field. 

·                  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. 

·                  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. 

Skill Requirements

·                  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 

·                  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 

·                  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.