Lead AI Engineer

EPAM Systems; Brasil; Presencial; BRL 250 000 - 520 000; Tempo integral

Resumo da oferta

EPAM Systems is seeking a Lead AI Engineer to design, build and scale cutting-edge AI applications powered by large language models. You will partner with clients to deliver tailored LLM-driven solutions, architect agentic systems and drive adoption of emerging AI technologies across enterprise environments.

You will design end-to-end AI applications, implement robust data pipelines, evaluate model performance and ensure security, observability and compliance while iterating on models with user

Qualificações

• Proficient in Python, Java, C#, or Go and modern web frameworks.
• Deep understanding of the AI development lifecycle including deployment and integration.
• Familiarity with major LLM platforms (OpenAI, Anthropic, Gemini) and related frameworks.
• Knowledge of AI integration patterns (RAG, agent orchestration, tool calling) and retrieval systems.
• Experience deploying AI solutions at scale with emphasis on performance, security and observability.
• Proven ability to evaluate generative AI quality using retrieval and evaluation metrics.

Responsabilidades

• Design, implement and maintain end-to-end AI applications (LLMs, chatbots, agents).
• Collaborate with clients to identify needs and tailor AI/LLM solutions.
• Architect data pipelines, prompts, and datasets for robust models.
• Monitor and optimize model performance for accuracy, security and scalability.
• Conduct experiments and rapid prototyping to validate feasibility and value.
• Stay updated with evolving LLM tech and methodologies to improve outcomes.
• Design agentic systems using LangChain, LangGraph and Semantic Kernel.
• Develop APIs and integrations for production AI applications.
• Deploy AI solutions with emphasis on cost-efficiency, observability and guardrails.
• Implement and monitor retrieval systems and ranking for agents.
• MLOps/AIOps practices for agentic systems and robust observability.
• Communicate complex concepts clearly to technical and non-technical stakeholders.

Conhecimentos

Python

Java

C#

Go

FastAPI

Streamlit

Gradio

LangChain

LangGraph

Semantic Kernel

LlamaIndex

RAG

MLOps

AIOps

APIs

LLM platforms

Vector databases

Ferramentas

LangChain

LangGraph

Semantic Kernel

LlamaIndex

Strands Agents

FAISS

Pinecone

Weaviate

ChromaDB

Qdrant

Databricks AgentBricks

Azure OpenAI

GCP Vertex AI

Descrição da oferta de emprego

We are seeking a Lead AI Engineer to design, build and scale cutting‑edge AI applications powered by large language models. In this role, you will partner with clients to deliver tailored LLM‑driven solutions, architect agentic systems and drive the adoption of emerging AI technologies across enterprise environments.

Responsibilities

• Design, implement and maintain end‑to‑end AI applications, including chatbots, Q&A platforms, agent workflows and other LLM‑driven solutions
• Collaborate directly with clients to understand their needs, identify opportunities and recommend tailored AI/LLM solutions that drive business value
• Architect and optimize robust data pipelines, prompt strategies and datasets to ensure effective, accurate and scalable AI models
• Evaluate, monitor and refine AI system performance, ensure outputs are accurate, secure, scalable and compliant with industry regulations and best practices
• Conduct research, design experiments and perform rapid prototyping to validate technical feasibility and demonstrate the business value of AI solutions
• Stay current with evolving LLM technologies, frameworks, protocols (such as MCP, A2A, ACP) and methodologies, continuously improve solution quality and client outcomes
• Design and implement agentic systems with frameworks such as LangChain, LangGraph and Semantic Kernel, integrate with vector databases and advanced memory architectures
• Develop and maintain APIs and system integrations for production‑grade AI applications, including enterprise system integration (CRM, ERP, databases)
• Deploy AI solutions at scale, consider performance, cost‑efficiency, maintainability, observability and security (including guardrails and prompt injection prevention)
• Implement and monitor retrieval systems (keyword search, vector search, embeddings), ranking algorithms and agent evaluation frameworks
• Use MLOps/AIOps practices for agentic systems and ensure robust observability and monitoring of deployed solutions
• Clearly communicate complex technical concepts and AI strategies to both technical and non‑technical stakeholders, iterate on models based on user feedback

Requirements

• Strong proficiency in at least one modern programming language (such as Python, Java, C#, Go, etc.); experience with web frameworks like FastAPI or similar is a plus
• Deep understanding of the AI application development lifecycle, including production deployment, system integration and rapid UI prototyping (Streamlit, Gradio or similar)
• Familiarity with major LLM platforms and APIs (OpenAI, Anthropic, Amazon Bedrock, Gemini) and related frameworks (LangChain, LangGraph, LlamaIndex, Strands Agents, etc.)
• Knowledge of advanced AI integration patterns (e.g., RAG, agent orchestration, tool calling), retrieval systems (keyword/vector search, embeddings) and ranking algorithms
• Experience to deploy AI solutions at scale, with a focus on performance, cost‑efficiency, maintainability, observability and security (including guardrails and prompt injection prevention)
• Proven ability to evaluate generative AI quality with retrieval/classification scores, LLM‑based evaluation, agent evaluation metrics and A/B testing
• Experience with vector databases (Pinecone, Weaviate, ChromaDB, FAISS) and semantic/hybrid search
• Experience to design experiments, conduct A/B tests and iterate on models based on user feedback
• Experience with enterprise system integration (CRM, ERP, databases) and deployment to cloud AI platforms or on‑premise solutions
• Experience with observability and monitoring tools/frameworks, and application of MLOps/AIOps practices for agentic systems
• Familiarity with emerging protocols (MCP, A2A, ACP) and advanced memory architectures
• Proven experience in AI engineering and delivery of ML‑based solutions in production environments
• Strong problem‑solving skills, attention to detail and ability to work independently and collaboratively
• Excellent communication, collaboration and interpersonal skills, with the ability to explain complex technical concepts to non‑technical stakeholders

Technologies

• Proficiency in at least one modern programming language (e.g., Python, Java, C#, Go, etc.) for AI development
• Web frameworks: FastAPI, Streamlit, Gradio, Flask, Spring Boot, ASP.NET or similar
• Major LLM platforms and APIs: OpenAI, Anthropic, Amazon Bedrock, Gemini
• Agentic frameworks: LangChain, LangGraph, Semantic Kernel, LlamaIndex, Strands Agents
• Data pipeline and integration tools
• Vector databases: Qdrant, FAISS, Chroma, Pinecone, Weaviate, ChromaDB
• Retrieval and ranking systems: keyword search, vector search, embeddings, ranking algorithms
• Cloud AI platforms: Azure OpenAI, Amazon Bedrock, GCP Vertex AI
• On‑premise solutions: vLLM
• Enterprise AI platforms: AWS AgentCore, Databricks AgentBricks, Google Agents Space, Azure AI Foundry
• Observability and monitoring tools/frameworks
• MLOps/AIOps practices for agentic systems
• Security and guardrail tools for AI applications
• Protocols: MCP, A2A, ACP
• Advanced memory architectures

EPAM is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, disability, protected veteran status, or any other characteristic protected by applicable law.