AI/ML Engineer – Agentic AI

Key Responsibilities

• Design and develop Agentic AI systems with multi-step reasoning, tool use, and workflow orchestration.
• Integrate AI agents into client applications and business workflows.
• Build, fine-tune, and evaluate ML/LLM models using established training platforms and pipelines.
• Collaborate with clients and engineering teams to gather requirements and translate business problems into AI solutions.
• Integrate agentic workflows using APIs, SDKs, and platform-specific tools across web, mobile, and enterprise applications.
• Develop RAG pipelines, vector databases, embeddings, and prompt/context engineering strategies.
• Define evaluation metrics and conduct model/agent performance experiments.
• Optimize AI solutions for scalability, latency, cost, security, observability, and reliability.
• Work with data engineering, product, and QA teams to deliver production-ready AI features.
• Document architecture, model behavior, integration patterns, and deployment processes.
• Stay updated on the Agentic AI and ML ecosystem and recommend relevant frameworks and platforms.

Required Skills & Experience

• 3–5 years of software engineering experience, including hands-on AI/ML application development.
• Experience building Agentic AI systems using LangChain, LangGraph, AutoGen, CrewAI, or similar frameworks.
• Experience integrating AI/ML solutions into web, mobile, or enterprise applications using APIs and SDKs.
• Knowledge of ML platforms such as AWS SageMaker, Google Vertex AI, Azure ML, or equivalent.
• Strong Python programming skills.
• Familiarity with JavaScript/TypeScript or mobile/native development is a plus.
• Strong understanding of LLMs, prompt engineering, embeddings, RAG, and vector databases.
• Experience with Pinecone, FAISS, Weaviate, or similar vector database technologies.
• Experience with AWS, Azure, or GCP cloud platforms.
• Hands-on experience with Docker and Kubernetes.
• Familiarity with MLOps, model versioning, ML CI/CD, monitoring, and observability.
• Strong client-facing communication and presentation skills.
• Ability to explain technical concepts to non-technical stakeholders.
• Experience in IT services/consulting environments is highly preferred.
• Ability to manage multiple client engagements and projects simultaneously.