Senior AI Engineer

Senior AI Engineer

GenAI · Conversational AI · Voice & Chat Bots · LLM Integration · Solution Architecture

ABOUT THE ROLE

We are a data and AI services firm seeking a Senior AI Engineer to lead the design and delivery of intelligent,

conversational, and agentic AI solutions for our clients. This is a hands-on, client-facing engineering role — you

will architect and build production-grade GenAI applications, voice and chat bots, and LLM-powered integrations

across a variety of industries and technology stacks.

You will be the technical authority on AI engagements — owning solution architecture, driving LLM selection and

fine-tuning decisions, and integrating AI capabilities into existing client systems. You will work closely with clients

through presales, discovery, and delivery, and will mentor junior engineers within the Data Practice.

KEY RESPONSIBILITIES

Conversational AI — Chat & Voice Bots

▸ Design and deliver production-ready chatbots and voicebots for client-facing and internal enterprise use

cases.

▸ Build real-time voice AI pipelines using LiveKit — handling audio streaming, VAD (voice activity detection),

STT/TTS integration, and turn management.

▸ Architect multi-turn, context-aware conversational flows with robust fallback handling and session state

management.

▸ Integrate speech-to-text (Whisper, Azure Speech, Deepgram) and text-to-speech (ElevenLabs, Azure TTS,

OpenAI TTS) providers based on client requirements.

▸ Ensure low-latency, high-availability voice and chat deployments suitable for customer-facing production

traffic.

LLM Integration & Orchestration

▸ Build LLM-powered applications using LangChain, LangGraph, and LlamaIndex — including RAG

pipelines, agents, and tool-calling workflows.

▸ Integrate OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, and open-source models (Llama,

Mistral, Phi) based on cost, latency, and compliance needs.

▸ Design and implement Retrieval-Augmented Generation (RAG) systems with vector stores (Pinecone,

Weaviate, pgvector, Azure AI Search).

▸ Build and manage AI agent frameworks — autonomous agents, multi-agent workflows, and human-in-the loop patterns.

▸ Develop prompt engineering strategies, prompt templates, and evaluation pipelines for consistent, reliable

LLM output.

Fine-Tuning & Model Customisation

▸ Fine-tune open-source LLMs (Llama 3, Mistral, Phi-3) using techniques such as LoRA, QLoRA, and PEFT

for domain-specific use cases.

▸ Manage fine-tuning pipelines end-to-end — dataset curation, preprocessing, training, evaluation, and

model registry management.

▸ Implement RLHF / DPO alignment techniques where applicable to align model outputs with client

expectations.

▸ Benchmark model performance using standardised and custom evaluation suites; iterate based on results.

Solution Architecture & Client Engagement

▸ Own AI solution architecture for client engagements — selecting the right models, frameworks, and

infrastructure patterns for each use case.

▸ Participate in presales — contribute to proposals, solution briefs, PoCs, and effort estimations for AI

projects.

▸ Lead client discovery sessions, translating ambiguous business requirements into concrete, executable AI

solution designs.

▸ Present architecture decisions and technical recommendations clearly to both technical and non-technical

client stakeholders.

▸ Handle multiple client engagements simultaneously, managing delivery timelines and technical quality

across projects.

Integration & Production Engineering

▸ Integrate AI capabilities into client systems via REST APIs, webhooks, and event-driven architectures.

▸ Deploy AI services on cloud platforms (Azure, AWS, GCP) using containerised (Docker, Kubernetes) and

serverless patterns.

▸ Implement observability for AI systems — tracing, logging, hallucination detection, and LLM performance

monitoring (LangSmith, Arize, Helicone).

▸ Ensure AI systems meet security, compliance, and data privacy standards — including PII handling, data

residency, and responsible AI guardrails.

▸ Build CI/CD pipelines for model deployment, versioning, and rollback in production environments.

Mentorship & Practice Development

▸ Mentor junior and mid-level AI engineers, conducting code reviews and guiding best practices in LLM

application development.

▸ Contribute to internal AI accelerators, reusable templates, and knowledge-sharing initiatives within the

practice.

▸ Stay current with rapidly evolving GenAI research and tooling; evaluate and advocate for adoption of

relevant advances.

REQUIRED SKILLS & TECHNOLOGIES

Python LangChain / LangGraph LlamaIndex

LiveKit OpenAI / Azure OpenAI Anthropic Claude

RAG Pipelines Vector Databases LLM Fine-Tuning (LoRA / QLoRA)

Prompt Engineering AI Agents & Orchestration Chat Bot Development

Voice Bot Development STT / TTS Integration REST API Integration

Docker / Kubernetes Azure / AWS / GCP Solution Architecture

Hugging Face Transformers Responsible AI & Guardrails LLM Observability

NICE TO HAVE

Microsoft Copilot Studio Semantic Kernel AutoGen / CrewAI

Deepgram / ElevenLabs LangSmith / Arize / Helicone MLflow / BentoML

Google Gemini / Vertex AI Multimodal AI (Vision + Audio) GraphRAG

EXPERIENCE & QUALIFICATIONS

▸ 2+ years of software engineering experience, with at least 1.5 years focused on AI/ML and GenAI

application development.

▸ Hands-on experience building and deploying LLM-based applications in production — RAG systems,

agents, chatbots, or voicebots.

▸ Strong Python skills; comfortable with async programming, API design, and working across multiple

frameworks simultaneously.

▸ Demonstrable experience with real-time voice AI pipelines or streaming audio applications (LiveKit,

WebRTC, or equivalent).

▸ Experience with at least one major cloud platform (Azure preferred) and containerised deployment.

▸ Prior experience in a services, consulting, or agency environment — managing client relationships and

delivery across multiple projects.

▸ Strong communication skills — able to explain complex AI concepts to non-technical stakeholders clearly

and credibly.

▸ Bachelor's or Master's degree in Computer Science, AI/ML, or related field — or equivalent practical

experience.

▸ Azure AI / OpenAI certifications or equivalent are a plus

Pay: ₹200,000.00 - ₹700,000.00 per year

Work Location: In person