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

58 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.