AI/ML ENGINEER

Tech Stack Core Language (Must-Have) ● Python (expert level) – primary language for agentic workflows, GenAI systems, and data pipelines ● Good to have exposure on Java (for backend integration), REST / GraphQL GenAI & Agentic Systems (Must-Have) ● LLMs: OpenAI GPT-4+, Claude, Gemini, SLMs ● Agent frameworks: LangChain, LangGraph, MCP, CrewAI ● Agent orchestration, tool-calling, multi-step workflows Data & RAG Platforms (Must-Have) ● Databricks, Spark / PySpark, Delta Lake ● RAG pipelines, embeddings, vector search ● Vector DBs: FAISS, Pinecone, Weaviate, Chroma (or similar)

Cloud & Production ● AWS (S3, ECS/EKS, Lambda, messaging services) ● APIs (REST / GraphQL), distributed systems ● Docker, Kubernetes, CI/CD Monitoring & MLOps (Good to Have) ● MLflow, Databricks Jobs / Workflows ● Observability: CloudWatch, Grafana, Prometheus Role : Senior AI Engineer (GenAI, Agentic Systems & Data Platforms) Location: Remote (Work from Home) We are hiring a Senior AI Engineer with deep Python expertise and hands-on experience building and optimizing agentic workflows in production. This role is ideal for someone who has evolved from a strong Data Science or Backend (SDE) foundation into agent-based GenAI systems, and enjoys owning systems end-to-end — from design to scale, performance, and reliability. You will work at the intersection of: ● LLMs & Agentic AI ● Databricks / Spark-based data platforms ● Cloud-native backend systems on AWS Key Responsibilities Agentic Systems & GenAI ● Design, build, and optimize agentic workflows using LangGraph, LangChain, MCP, CrewAI, or similar frameworks. ● Build multi-step, tool-calling AI agents that automate real enterprise workflows (CRS onboarding, sales ops, business rules, analytics). ● Own agent orchestration, state management, retries, fallbacks, and error handling in production systems. ● Continuously improve agent performance across latency, cost, accuracy, and determinism.

Data & RAG Platforms ● Build RAG pipelines on Databricks, leveraging Spark/PySpark for: ○ Large-scale document ingestion (PDFs, specs, contracts) ○ Chunking, embeddings, indexing, and retrieval ● Integrate structured (tables, metrics, logs) and unstructured data into agent-driven systems. Production Engineering ● Build and deploy LLM-powered APIs and services using Python on AWS. ● Collaborate with Backend and Data Platform teams to productionize workflows using Databricks Jobs, MLflow, CI/CD, and cloud services. ● Implement guardrails, monitoring, observability, and evaluation for agent behavior in production. ● Ensure systems meet enterprise-grade reliability, scalability, and cost efficiency standards. Mandatory Requirements Experience ● 5–8 years of overall experience in Data Science, Machine Learning, or Backend (SDE) roles. ● Minimum 2 years of hands-on experience building agentic or workflow-driven AI systems in production. Core Skills (Non-Negotiable) ● Expert-level Python — this is mandatory and core to the role. ● Proven experience designing and shipping production GenAI systems, not just prototypes. ● Strong hands-on experience with Databricks, Spark / PySpark, and data pipelines. ● Practical experience with LLMs, RAG pipelines, embeddings, and vector search. ● Experience working with AWS-native architectures (S3, ECS/EKS/Lambda, messaging systems). ● Solid engineering fundamentals: APIs, distributed systems, CI/CD, Docker/Kubernetes.

Nice to Have ● Experience with Databricks Vector Search, MLflow, Feature Store. ● Understanding of LLM internals, prompt optimization, inference tuning, and SLM strategies. ● Experience building cost-efficient, low-latency AI systems at scale. ● Familiarity with enterprise workflow automation (sales ops, support, analytics). ● Domain exposure to travel-tech, marketplaces, pricing/availability systems.

Why Join Us? ● Work on real production agentic systems at massive scale — not demos or POCs. ● Direct impact on GMV growth, revenue yield, and operational automation. ● Ownership of core AI infrastructure in a fast-growing, VC-backed company. ● Strong engineering culture with deep focus on performance, cost, and reliability. ● Opportunity to help define the agent-driven foundation of a global B2B platform.