Technical Lead (Agentic Framework AI)_2678

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.