AI/ML Lead

Job Role- AI/ML Lead

Job Type- Contract/Freelance

Duration- 2 months

No. hours- 8 hours per day

Work Mode- Remote

About the Role

AI/ML Technical Lead

Key Responsibilities

• AI System Architecture:

  Design the architecture of the AI-powered agent including LLM-based conversation workflows, voice bots, and follow-up orchestration.

• Model Integration & Prompt Engineering:

  Leverage APIs from OpenAI, Anthropic, or deploy open models (e.g., LLaMA 3, Mistral). Implement effective prompt strategies and retrieval-augmented generation (RAG) pipelines for contextual responses.

• Data Pipelines & Knowledge Management:

  Build secure data pipelines to ingest, embed, and serve tenant-specific knowledge bases (FAQs, scripts, product docs) using vector databases (e.g., Pinecone, Weaviate).

• Voice & Text Interfaces:

  Implement and optimize multimodal agents (text + voice) using ASR (e.g., Whisper), TTS (e.g., Polly), and NLP for automated qualification and call handling.

• Conversational Flow Orchestration:

  Design dynamic, stateful conversations that can take actions (e.g., book meetings, update CRM records) using tools like LangChain, Temporal, or n8n.

• Platform Scalability:

  Ensure models and agent workflows scale across tenants with strong data isolation, caching, and secure API access.

• Lead a Cross-Functional Team:

  Collaborate with backend, frontend, and DevOps engineers to ship intelligent, production-ready features.

• Monitoring & Feedback Loops:

  Define and monitor conversation analytics (drop-offs, booking rates, escalation triggers), and create pipelines to improve AI quality continuously.

Qualifications

Must-Haves:

• 5+ years of experience in ML/AI, with at least 2 years leading conversational AI or LLM projects.
• Strong background in NLP, dialog systems, or voice AI — preferably with production experience.
• Experience with OpenAI, or open-source LLMs (e.g. LLaMA, Mistral, Falcon) and orchestration tools (LangChain, etc.).
• Proficiency with Python and ML frameworks (Hugging Face, PyTorch, TensorFlow).
• Experience deploying RAG pipelines, vector DBs (e.g. Pinecone, Weaviate), and managing LLM-agent logic.
• Familiarity with voice processing (ASR, TTS, IVR design).
• Solid understanding of API-based integration and microservices.
• Deep care for data privacy, multi-tenancy security, and ethical AI practices.

Nice-to-Haves:

• Experience with CRM ecosystems (e.g. Salesforce, HubSpot) and how AI agents sync actions to CRMs.
• Knowledge of sales pipelines and marketing automation tools.
• Exposure to calendar integrations (Google Calendar API, Microsoft Graph).
• Knowledge of Twilio APIs (SMS, Voice, WhatsApp) and channel orchestration logic.

Familiarity with Docker, Kubernetes, CI/CD, and scalable cloud infrastructure (AWS/GCP/Azure).