> Sr. AI Engineer

Location: Hyderabad, INDIA

Work Mode: Hybrid

Skills: Python;Azure;LLMs;Gen AI;Agentic AI;LangChain;LangGraph;Semantic Kernel;MS Auto Gen

Experience in Years: 10

Type: Full Time

Sr. AI Engineer - Generative AI Agentic AI

Location: Hyderabad (Hybrid)

1. JOB PURPOSE:

- 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.

2. KEY ACCOUNTABILITIES:

Generic Accountability

- Design and build enterprise-grade Generative AI applications using Large Language Models (LLMs) across business and technology use cases.
- Develop Agentic AI solutions including autonomous agents, multi-agent orchestration, tool-driven workflows and decision support systems.
- Implement robust Retrieval-Augmented Generation (RAG) architectures using embeddings, vector

databases, semantic search and optimized retrieval strategies.
- Integrate AI services with enterprise APIs, middleware, backend systems and client channels such as web and mobile applications.
- Apply prompt engineering, tool/function calling, memory management, context engineering and agent orchestration patterns to improve quality, latency and cost efficiency.
- Deploy scalable AI workloads on Azure and AWS cloud platforms using up-to-date DevOps, CI/CD, container and Kubernetes practices.
- Establish model evaluation, guardrails, observability, security controls and AI governance practices aligned to enterprise standards.
- Collaborate with product owners, enterprise architects, engineering teams and business stakeholders to convert business needs into secure, reusable and sustainable AI capabilities.
- Discover technical debt and continuous improvement opportunities in existing systems and influence the technical backlog with architecture and product stakeholders.
- Mentor engineers, promote engineering craftsmanship, and support communities of practice for AI engineering, cloud-native development and automation.

3. JOB CONTEXT

Specific Accountability

- Build production-grade AI agents using LangChain, LangGraph, Microsoft Semantic Kernel and Microsoft

AutoGen; working experience with Semantic Kernel and AutoGen is mandatory.
- Design and implement multi-agent workflows covering planning, tool use, state management, memory, feedback loops,



human-in-the-loop controls and workflow-driven decisions.
- Architect end-to-end data indexing pipelines optimized for semantic search, RAG quality, retrieval latency and answer relevancy.
- Design resilient data ingestion frameworks that ensure high availability and low latency for vectorized datasets.
- Implement context engineering strategies to reduce token utilization, manage prompt/context windows and improve end-to-end inference latency. - Evaluate and improve AI agent performance using relevant metrics including response quality, grounding, retrieval precision/recall, faithfulness, latency, cost, safety and task completion.
- Deploy and manage models using Azure OpenAI Service, Amazon Bedrock and Google Vertex AI where required by solution constraints.
- Integrate pre-built AI APIs for speech-to-text, computer vision and natural language processing where they add business value.
- Leverage serverless architectures such as AWS Lambda and Azure Functions to trigger AI inference, data pre-processing and asynchronous workflow steps.
- Implement CI/CD pipelines with code quality checks, static code analysis, security scanning, requirement traceability and Jira integration.
- Operate monitoring, logging and alerting for AI, application and cloud environments with measurable reliability and supportability.
- Ensure SDLC, security, data protection, compliance and architecture governance procedures are followed for AI-powered services. Added Advantage - Working experience in building agents using Microsoft Agent Framework or equivalent Microsoft-native agent development capabilities.
- Practical experience with responsible AI controls, AI red-teaming, policy-based guardrails and enterprise AI governance.

4. 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.

ANNEXURE: TECHNICAL BEHAVIOURAL COMPETENCIES

Technical Competencies

- Generative AI solution design and enterprise LLM application development.
- Agentic AI engineering using Semantic Kernel, AutoGen, LangChain, LangGraph and related frameworks.
- RAG architecture, embeddings, vector database design, semantic search and retrieval optimization.
- Cloud-native deployment, Kubernetes operations, CI/CD automation and observability for AI workloads.
- Responsible AI, model evaluation, guardrails, governance, security and operational resilience.

Behavioural Competencies
- Collaboration, ownership, engineering craftsmanship and continuous improvement.
- Stakeholder management, clear communication, mentoring and cross-functional leadership.
- Analytical thinking, pragmatic decision making, curiosity and growth mindset.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Sr. AI Engineer (Hyderabad)
🏢 CloudFulcrum
📍 Hyderabad