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

▸ 5+ years of software engineering experience, with at least 2–3 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.

Please share your candidature at prerna.gavale@bluebenz.com

Work Location: In person