Desired Candidate Profile

• 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.
• Skills
• Generative AI
• Python
• Kubernetes