Artificial Intelligence (AI) Engineer

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.

• 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 NumX, pandas, FastAPI and hands‑on experience with PyTorch or Tensor Flow.
• Hands‑on experience with Lang Chain and Lang Graph; mandatory working experience with Microsoft Semantic Kernel and Microsoft Auto Gen.
• 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 Git Lab, Dev Ops tool chains, 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.

• 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, Auto Gen, Lang Chain, Lang Graph and similar agent frameworks.
• Python, FastAPI, PyTorch/Tensor Flow, REST APIs, microservices, serverless functions and event‑driven integration.
• Azure, AWS, Kubernetes, containers, CI/CD, Dev Ops automation, monitoring and secure software delivery.

• 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.