AI Engineer (Remote)

TeamLogicIT; United States; Remoto; USD 150.000 - 190.000; Tempo pieno

Vantaggi offerti da questo lavoro

Industry-leading compensation

HR benefits

Descrizione del lavoro

TeamLogicIT is seeking an AI Engineer to design, build, and deploy production-grade LLM solutions on Azure, focusing on RAG pipelines and agentic AI systems. You will architect multi-agent workflows using LangChain, LangGraph, CrewAI, AutoGen, and LlamaIndex, and integrate AI with enterprise systems via REST APIs and webhooks.

You will lead MLOps, telemetry-driven improvements, and user adoption, while mentoring junior engineers and presenting to clients and stakeholders across industries.

Competenze

• 2+ sentences: 4–6 years hands-on Generative AI / LLM solution deployment experience.
• Proficiency in Python for AI development, API integration, and backend services (FastAPI, REST).
• Experience with multi-agent/workflow frameworks and RAG pipelines.

Mansioni

• Design, build, and deploy production-grade LLM solutions on Azure.
• Architect multi-agent workflows using LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex.
• Integrate AI with enterprise systems via REST APIs and webhooks (Jira, ServiceNow, CRMs, ERPs).
• Lead MLOps, evaluation pipelines, and telemetry-driven improvements.
• Mentor and enable teams; present to clients and stakeholders.

Conoscenze

Python

LLM frameworks

Azure OpenAI

REST APIs

LangChain

LangGraph

AutoGen

CrewAI

LlamaIndex

API integration

English communication

Secure design

Strumenti

LangChain

LangGraph

AutoGen

CrewAI

LlamaIndex

Azure OpenAI Service

Azure AI Search

Azure Data Factory

Jira

ServiceNow API

Descrizione del lavoro

Position Name - AI Engineer

Responsibilities

• Design, build, and deploy production-grade LLM solutions, RAG pipelines, and agentic AI systems on cloud platforms (primarily Azure).
• Conduct AI readiness workshops, map business processes, and define automation ROI through intelligent AI solutions.
• Architect and implement multi-agent workflows using frameworks such as LangChain, LangGraph, CrewAI, and AutoGen.
• Integrate AI solutions with enterprise systems (ServiceNow, Jira, CRMs, ERPs) via REST APIs and webhooks.
• Apply advanced LLM techniques including prompt engineering, Retrieval-Augmented Generation (RAG), LLM fine-tuning, and tool/function calling with secure, scalable design.
• Monitor performance, drive adoption, and iterate based on telemetry, evaluation pipelines, and user feedback.

Client Discovery & Solutioning

• Run AI readiness workshops, map current processes, and quantify automation ROI.
• Translate business requirements into technical agent designs and LLM-based solution architectures.
• Conduct client demos, manage technical Q&A, and communicate AI solutions clearly to both technical and non-technical stakeholders.

Agent & Automation Development

• Build conversational and task-oriented AI agents using low/no-code and Python-native agentic frameworks (LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex).
• Design and implement RAG pipelines with vector stores (ChromaDB, FAISS, Pinecone, Qdrant, OpenSearch) and semantic search over enterprise data.
• Integrate AI solutions with enterprise applications (M365, Jira, ServiceNow, CRMs, ERPs) via APIs and webhooks.
• Implement Model Context Protocol (MCP) servers and clients for standardised, tool-based agentic workflows.
• Apply data-governance guardrails, prompt engineering best practices, and observability/logging across all deployments.

MLOps & Continuous Improvement

• Configure evaluation pipelines, drift monitoring, and automated performance-improvement triggers.
• Analyse usage telemetry and user feedback; iterate agent designs to maximise adoption and business impact.
• Lead fine-tuning efforts on models such as GPT-4o, Gemini, and open‑source LLMs (Llama) to improve intent recognition and output quality.

Security & Compliance

• Perform threat modelling and apply security guardrails to LLM pipelines (prompt injection defence, data privacy, GxP or equivalent compliance where applicable).
• Collaborate with cybersecurity stakeholders on secure AI architecture design and deployment practices.

Thought Leadership & Enablement

• Document reusable patterns, RAG architectures, prompt templates, and agentic playbooks.
• Mentor junior engineers on prompt engineering, Python development, and LLM best practices.
• Present at client webinars, internal peer groups, and industry events.

Skills, Knowledge, and Experience

• 4-6 years of hands‑on experience building and deploying production‑grade Generative AI / LLM solutions.
• Strong proficiency in Python for AI development, API integration, and backend services (FastAPI, RESTful APIs).
• Hands‑on experience with LLM orchestration frameworks: LangChain, LangGraph, AutoGen, CrewAI, or LlamaIndex.
• Proven experience designing and deploying RAG pipelines with vector stores (ChromaDB, FAISS, Pinecone, Qdrant, or similar).
• Solid understanding of Azure AI services: Azure OpenAI Service, Azure AI Search, Azure AI Services, Azure Data Factory.
• Experience integrating AI systems with enterprise tools (Jira, ServiceNow, CRMs, ERPs) via REST APIs and webhooks.
• Strong understanding of LLM security guardrails, prompt injection risks, and data governance.
• Excellent English communication skills; ability to engage clients, run workshops, and present technical concepts clearly.
• 4+ hour overlap with U.S. Eastern Time (for remote India-based roles).

Preferred Skills and Qualifications

• Hands‑on experience with multi‑agent systems and Model Context Protocol (MCP).
• Experience with Python Programming and LLM fine‑tuning on models such as GPT-4o, Gemini, or open‑source Llama variants.
• Familiarity with containerisation and deployment tools: Docker, Git, CI/CD pipelines.
• Experience with graph databases (Neo4j) or knowledge graphbased AI architectures.
• Past AI deployments in domains such as IT operations, pharma, QA/testing, Sales, HR, or Finance.
• Certifications: Microsoft AI‑102 (Azure AI Engineer Associate), Microsoft DP‑100 (Azure Data Scientist), or Google Cloud Professional ML Engineer.

Industry

Information Technology / Managed Services

Employment Type

Full‑Time

Benefit

Industry‑leading Compensation + HR Benefits.