> Markdown version of [/jobs/ext/4623217-ai-ml-engineer](https://www.wearedevelopers.com/jobs/ext/4623217-ai-ml-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# AI/ML Engineer

- **Company:** eTeam Inc
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Microsoft Azure, Databases, Data Governance, Data Systems, Memory Management, Python (Programming Language), Systems Development Life Cycle, SQL Databases, Enterprise Software Applications, LangChain, Retrieval-Augmented Generation, Snowflake, Multi-Agent Systems, Prompt Engineering, Apache Spark, Generative AI, AI Coding Agents, Agentic-AI, Event Driven Architecture, Containerization, Pyspark, Data Management, Semantic Kernel, Databricks
- **Published:** November 7, 2026
- **Apply:** https://www.thejobnetwork.com/job/7b470809-34b0-4ef6-a50e-63408b6bb474/aiml-engineer

## About the Role

Python, PySpark, SQL, Databricks, Snowflake
GenAI, Agentic AI, ClientP Architecture, RAG
LangChain, LangGraph, Semantic Kernel

Minimum years of experience
>10 years

Top 3 responsibilities:
Coding
Testing
Deployment

## Description

Must Have Skills
Gen AI, AI/ML, RAG, LangChain, Python, Spark, Snowflake

Nice to have skills
Azure

Detailed Job Description
Design and develop scalable AI-powered data platforms and agentic AI solutions that enable autonomous decision-making, intelligent workflow orchestration, and business process automation.
Build and maintain data engineering pipelines using Python, Spark, Databricks, Snowflake, SQL, and cloud-native technologies to support AI/ML, GenAI, and analytics workloads.
Architect and implement multi-agent AI systems with capabilities such as goal decomposition, task planning, reasoning, memory management, self-reflection, tool utilization, and autonomous execution.
Develop and optimize Retrieval-Augmented Generation (RAG) solutions, including document ingestion, chunking, embedding generation, vector database integration, retrieval optimization, and response grounding.
Integrate AI agents with enterprise systems, APIs, databases, knowledge repositories, CI/CD pipelines, and event-driven architectures to create end-to-end intelligent automation frameworks.
Design and implement data governance, security, guardrails, monitoring, and compliance controls to ensure responsible AI adoption and protection of enterprise data assets.
Build reusable AI engineering components using modern frameworks such as LangChain, LangGraph, Semantic Kernel, ClientP-based architectures, and agent orchestration platforms.
Evaluate and improve AI system performance through prompt engineering, agent testing frameworks, model benchmarking, retrieval accuracy analysis, latency optimization, and continuous feedback loops.
Collaborate with architects, data scientists, application teams, and business stakeholders to translate complex business requirements into scalable AI-driven data solutions and production-ready platforms.
Provide technical leadership and mentorship by driving AI engineering best practices, conducting architecture reviews, developing reusable accelerators, and enabling engineering teams to leverage AI-assisted software development and agentic SDLC methodologies.