Gen AI

Position: Generative AI Engineer

Experience: 5 to 10 years

Notice Period: 0-90 Days

Location: Hyderabad

Required Information

Job Description – Software Engineer (LLM/GenAI Focus)

About the Role

This is a Full Stack Software Development Engineer role within the conversational AI platform (customer/associate-facing) team, responsible for building next-generation conversational AI, intelligent search, and knowledge systems that power customer and associate experiences across multiple channels.

This role requires hands-on expertise in designing, developing, and productionizing scalable, secure, and enterprise-grade GenAI solutions across cloud and on‑premise environments.

Key Responsibilities

• Design and develop LLM-powered applications for conversational AI, knowledge retrieval, and insights delivery
• Build and orchestrate end-to-end GenAI pipelines, including prompt engineering, RAG, and agent-based workflows
• Develop and manage LLM agents (tool usage, API integration, function calling)
• Integrate LLMs across cloud and on-prem/open-source environments
• Build RAG data pipelines (chunking, indexing, metadata enrichment)
• Implement evaluation frameworks covering accuracy, hallucination, latency, safety, and validation (LLM-as-judge, human-in-loop)
• Apply AI safety and guardrails, including prompt injection prevention and response validation
• Optimize models using prompt tuning, embeddings, and fine-tuning techniques
• Develop multimodal conversational experiences (text and voice)
• Build scalable backend services using microservices and REST APIs
• Enable observability (latency, token usage, cost, drift, quality monitoring)
• Contribute to POCs, experimentation, and adoption of emerging GenAI technologies

Required Skills

• Core Engineering
• Strong proficiency in:
• Java / J2EE, Spring Boot, RESTful APIs
• Python for AI/ML and GenAI workflows

• Experience in building distributed systems and microservices architectures

• GenAI & LLM Expertise
• Hands-on experience with LLM frameworks (LangChain, LangGraph, Semantic Kernel) and RAG-based knowledge retrieval systems
• Strong understanding of prompt engineering, workflow orchestration, and agent-based systems (tool usage, function calling)
• Experience with vector databases, embeddings, and semantic search
• Experience working with LLMs across cloud platforms and on-prem/open-source models
• Experience designing LLM evaluation pipelines (benchmarking, prompt testing, LLM-as-judge)
• Strong understanding of hallucination mitigation, AI safety, guardrails, and prompt injection prevention
• Knowledge of PII handling and governance/compliance frameworks
• Experience with distributed data and platform components, including:
• NoSQL databases (Cassandra) and caching (Redis)
• CI/CD pipelines and DevOps practices

• Container platforms (Kubernetes / OpenShift)

• Desired Skills
• Experience with Speech-to-Text (STT) and Text-to-Speech (TTS) integrations
• Familiarity with GenAI observability (latency, drift, token usage, cost monitoring)
• Experience with experimentation frameworks (A/B testing for prompts and models)
• Exposure to real-time/streaming AI systems (token streaming, WebSockets)
• Understanding of multi-agent systems and autonomous workflows
• Exposure to enterprise-scale, customer-facing AI platforms

Nice-to-Have Differentiators

• Experience with production-scale GenAI deployments
• Familiarity with cost optimization strategies for LLM workloads
• Exposure to open-source LLMs (LLaMA, Mistral, etc.)
• Contributions to GenAI/ML open-source ecosystems

Soft Skills

• Strong communication and stakeholder management skills
• Ability to operate effectively in fast-paced, ambiguous environments
• High ownership with focus on scalability, reliability, and quality
• Collaborative mindset with experience working across global teams