Agentic AI Engineer

As an Agentic AI Engineer, you will architect, design, and deploy advanced agentic AI solutions, leveraging Generative AI, Retrieval-Augmented Generation (RAG), and cutting-edge frameworks to solve complex business challenges. You’ll work at the intersection of AI research, cloud engineering, and enterprise architecture, collaborating with cross-functional teams to deliver robust, scalable, and compliant agentic systems.

What you do

Design and implement AI agents and autonomous systems using generative AI and RAG frameworks.
Integrate agentic frameworks and MCP servers into cloud environments (Azure, AWS), orchestrating multi-agent systems for high availability and performance.
Lead the evaluation and selection of AI/ML frameworks and tools, making informed design choices that balance technical trade-offs and business requirements.
Collaborate with data engineers, software developers, and product teams to build end-to-end AI solutions, from prototyping to production deployment.
Embed security, compliance, and Responsible AI principles from the outset. Implement guardrails and governance controls to ensure solutions meet regulatory and policy standards.
Drive innovation in agentic AI architectures, contributing to internal best practices, reusable components, and reference implementations.
Work closely with risk, security, and platform teams to address broader technical considerations (scalability, reliability, observability). Mentor junior engineers and foster knowledge sharing to elevate team capabilities.

What you bring

Deep AI Expertise: Strong knowledge of Generative AI, RAG techniques, and agentic AI concepts. Hands-on experience developing AI agents or autonomous systems.
Technical Proficiency: Proficiency in programming (Python and/or Java/C#) and AI frameworks. Experience with agent orchestration tools or MCP servers is a plus.
Cloud Engineering Skills: Proven experience deploying AI solutions on Azure and/or AWS (e.g., Azure AI services, AWS AI/ML offerings). Familiarity with containerization (Docker, Kubernetes) and infrastructure-as-code (Terraform) for AI deployments.
Framework & Architecture Knowledge: Demonstrated ability to evaluate and select appropriate ML frameworks, libraries (e.g., LangChain, PyTorch, TensorFlow), and to design system architectures for AI solutions.
MLOps & Systems Knowledge: Understanding of ML lifecycle tools (MLflow, CI/CD pipelines) and distributed systems. Ability to design for scalability, monitoring, and maintenance of AI services.
Responsible AI & Compliance: Awareness of model risk management, data privacy, and Responsible AI guidelines. Experience integrating compliance requirements into technical solutions.
Collaboration & Communication: Excellent communication skills with ability to convey complex AI concepts to both technical and non-technical stakeholders. Strong team player comfortable working in cross-functional, distributed teams.
Ownership & Impact: A results-driven mindset with a track record of owning projects from concept to production. Ability to navigate ambiguity and drive structured, high-quality outcomes.

Ideal Candidate Profile

5+ years in software or AI engineering, with at least 2 years focused on agentic AI systems or applied Generative AI.
Expert in Python (and familiar with one other programming language); experience with AI/LLM frameworks (e.g., LangChain, Transformers), Cloud AI services (Azure AI/OpenAI, AWS SageMaker/Bedrock), and DevOps (Kubernetes, Docker, Terraform). Knowledge of MCP servers or multi-agent orchestration platforms is highly valued.
Proven ability to design and build AI agent systems, make strategic framework/architecture decisions, and ensure solutions are scalable, secure, and compliant. Equally comfortable prototyping new ideas and engineering production-grade systems.

1 Job for System Architecture in India

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