Artificial Intelligence Engineer

Role Overview

AI Tech Lead who is first a builder, and second a product translator

You’ll wear multiple hats:

• 40%

  Build AI applications and features for our partners & customers.

• 25%

  Develop internal AI platform & infrastructure.

• 15%

  Drive AI adoption & enablement across teams.

• 10%

  Ensure data quality, governance, and compliance.

• 10%

  Explore new AI capabilities and run PoCs.

Key Responsibilities:

AI Application Development (40%)

• Design and implement AI features in PB Partners’ apps (e.g., LLM-powered support, recommendation engines, document parsing).
• Work with product teams to turn business problems into AI-powered workflows.

AI Platform & Infra (25%)

• Build and maintain scalable AI pipelines, APIs, and tools
• Manage MLOps, model hosting, and performance optimization (latency, cost, reliability).

AI Adoption & Enablement (15%)

• Create internal playbooks, templates, and reusable components.
• Run training sessions and hackathons to increase AI literacy.

Data & Governance (10%)

• Set up secure data pipelines and ensure compliance with IRDAI, PII rules, and ethical AI guidelines.
• Implement bias detection and monitoring tools.

Research & Innovation (10%)

• Prototype next-gen capabilities (multi-modal models, agentic AI workflows).
• Evaluate emerging AI tools and frameworks for business fit.

What We’re Looking For

Must-Have

• 5–8 years of experience in backend engineering, MLOps, or full-stack AI solution delivery.
• Proven track record of shipping AI features to production (LLM apps, RAG systems, model fine-tuning, etc.).
• Strong in Python/Node.js, API integrations, and cloud platforms (AWS/GCP/Azure).
• Experience with AI frameworks (LangChain, LlamaIndex, Hugging Face) and vector DBs (Pinecone, Weaviate, FAISS).
• Understanding of BFSI/insurance tech or willingness to learn fast.

Good-to-Have

• Experience in BFSI regulatory compliance & data security.
• Exposure to Indian insurance ecosystem (POSP, GI products).
• Track record in running internal training/adoption programs.
• Comfort with low-code/no-code AI tools for rapid prototyping.

Mindset & Soft Skills

• 60% Builder: Hands-on coding and deployment.
• 30% Product Translator: Ability to align AI tech with business goals.
• 10% Evangelist: Inspire teams, run demos, document best practices.
• High bias for action — ship MVPs in weeks, not months.
• Comfortable juggling infra, application, and adoption work.