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.
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.
Pay: ₹672,253.66 - ₹868,409.32 per year
Benefits:
- Flexible schedule
- Provident Fund
Work Location: In person
