Minimum Qualification

• Bachelor’s degree in Computer Science, Software Engineering, Information Technology, Data Science, Artificial Intelligence or a related discipline.

• Relevant cloud, AI engineering, machine learning or architecture certifications are preferred.

Minimum Experience :-

• Senior professional with around 10 years of total software engineering, architecture, cloud or platform engineering experience.

• Minimum 3+ years of relevant hands-on AI Engineering experience, including Generative AI and practical LLM-based application delivery.

• Strong proficiency in Python, including NumPy, pandas, FastAPI and hands-on experience with PyTorch or TensorFlow.

• Hands-on experience with LangChain and LangGraph; mandatory working experience with Microsoft Semantic Kernel and Microsoft AutoGen.

• Experience implementing RAG using embeddings, vector databases, semantic search, retrieval optimization and model evaluation techniques.

• Experience deploying and managing models using Amazon Bedrock, Azure OpenAI Service and Google Vertex AI.

• Hands-on experience with microservices, containers, APIs, event-driven architecture, cloud-native services and evolutionary architecture practices.

• Experience managing and deploying AI workloads on Kubernetes in cloud-native and/or hybrid environments.

• Experience with CI/CD tools such as Jenkins or GitLab, DevOps toolchains, configuration management and cloud/on-prem deployment pipelines.

• Experience setting up pipelines with static code analysis, requirement tagging in Jira, quality gates and release governance.

• Experience operating monitoring tools for traditional infrastructure, cloud environments and AI-enabled business applications.

• Strong hands-on problem-solving mindset with the ability to analyze trade-offs and deliver sustainable, secure and high-quality solutions.

Key Technical Skills

• Generative AI, Agentic AI, autonomous agents, multi-agent orchestration and workflow-based AI systems.

• LLMs, embeddings, vector databases, RAG, semantic search, model evaluation, guardrails, observability and AI governance.

• Semantic Kernel, AutoGen, LangChain, LangGraph and similar agent frameworks.

• Python, FastAPI, PyTorch/TensorFlow, REST APIs, microservices, serverless functions and event-driven integration.

• Azure, AWS, Kubernetes, containers, CI/CD, DevOps automation, monitoring and secure software delivery.

Behavioural / Leadership Skills

• Strong collaborative mindset for agile architecture and decentralized decision making.

• Proactive, positive and growth-oriented leadership style with the ability to motivate engineers and foster craftsmanship.

• Strong communication, stakeholder engagement and influencing skills across product, business, architecture and engineering teams.

• Analytical, system-thinking and pragmatic problem-solving approach with commitment to product quality.