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.
QUALIFICATIONS & EXPERIENCE:
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