AI Engineer – Generative AI & Agentic AI

We are looking for an experienced AI Engineer responsible for designing developing and deploying Generative AI and Agentic AI solutions that support enterprise-scale business use cases The role will build intelligent systems that integrate complex backend services and client-facing applications across web mobile and enterprise platforms The primary responsibility is to design and develop AI-powered applications autonomous agents and multi-agent workflows while coordinating with cross-functional teams across architecture engineering product and business functions A commitment to collaborative problem solving sophisticated design and product quality is essential This role requires strong hands-on engineering experience practical working knowledge of modern agent frameworks and the ability to deliver secure scalable observable and governed AI solutions in cloud-native environments

• 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 Num Py, 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.

Generative AIPython Kubernetes