Senior AI Engineer (Baner)

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 .