AI/ML ENGINEER

Tech StackCore Language (Must-Have)● Python (expert level) – primary language for agentic workflows, GenAI systems, anddata pipelines● Good to have exposure on Java (for backend integration), REST / GraphQLGenAI & Agentic Systems (Must-Have)● LLMs: OpenAI GPT-4+, Claude, Gemini, SLMs● Agent frameworks: LangChain, LangGraph, MCP, CrewAI● Agent orchestration, tool-calling, multi-step workflowsData & 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/CDMonitoring & MLOps (Good to Have)● MLflow, Databricks Jobs / Workflows● Observability: CloudWatch, Grafana, PrometheusRole : 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-onexperience building and optimizing agentic workflows in production.This role is ideal for someone who has evolved from a strong Data Science orBackend (SDE) foundation into agent-based GenAI systems, and enjoys owningsystems 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 AWSKey ResponsibilitiesAgentic 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 errorhandling in production systems.● Continuously improve agent performance across latency, cost, accuracy, anddeterminism.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-drivensystems.Production Engineering● Build and deploy LLM-powered APIs and services using Python on AWS.● Collaborate with Backend and Data Platform teams to productionize workflows usingDatabricks Jobs, MLflow, CI/CD, and cloud services.● Implement guardrails, monitoring, observability, and evaluation for agent behavior inproduction.● Ensure systems meet enterprise-grade reliability, scalability, and cost efficiencystandards.Mandatory RequirementsExperience● 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 AIsystems 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 justprototypes.● Strong hands-on experience with Databricks, Spark / PySpark, and datapipelines.● 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 SLMstrategies.● 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 orPOCs.● 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, andreliability.● Opportunity to help define the agent-driven foundation of a global B2Bplatform.

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