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

Work arrangement
No