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.

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.

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