AI Engineer

What Youll Do

1. Model Integration: Deploy and optimize LLMs, embeddings, and RAG pipelines into production applications.

2. AI Microservices: Build scalable Python-based services for knowledge extraction, question answering, and domain-specific reasoning.

3. Evaluation & Monitoring: Implement frameworks to measure precision, recall, latency, and drift; set up alerts for model degradation.

4. Prompt & Context Engineering: Design context windows, prompt templates, and fine-tuning strategies for insurance-specific tasks.

5. Data Pipelines: Collaborate with data scientists to create deterministic, auditable flows from raw documents to model-ready inputs.

6. Security & Compliance: Ensure models are deployed with guardrails, logging, and data isolation to meet SOC2/HIPAA standards.

7. Cross-Functional Collaboration: Partner with product, compliance, and engineering to ship features that balance innovation with reliability.

What Were Looking For

1. Experience: 4+ years in applied AI/ML engineering,



with production experience (not just prototypes).

2. Core Skills: Deep proficiency with Python, FastAPI/Flask, and ML libraries (Transformers, LangChain, PyTorch/TensorFlow).

3. LLM Expertise: Hands-on experience integrating LLM APIs, fine-tuning, embeddings, or retrieval systems.

4. Cloud & Infra: Familiarity with deploying AI workloads on AWS/Azure/GCP; exposure to vector DBs (Pinecone, Weaviate, FAISS).

5. Evaluation Mindset: Experience building structured evaluation and monitoring for AI systems.

6. Startup DNA: Comfortable with ambiguity and rapid iteration; thrives in lean environments.

Nice to Have:

1. Background in insurance or financial services data.

2. Experience with orchestration frameworks (LangGraph, Airflow, Prefect).

3. Familiarity with secure deployment patterns (VPC, single-tenant isolation, audit logging).

📌 Backend AI Engineer (Bengaluru)
🏢 Ankyah Infinity
📍 Bengaluru