AI Engineer

ES TECH CONSULTANCY PTE. LTD.; Região Norte; On-site; BRL 180,000 - 300,000; Full time

Job summary

ES TECH CONSULTANCY PTE. LTD. seeks an AI Software Engineer (LLM, GenAI & Agentic Systems) to design, build, and deploy production-grade AI solutions leveraging LLMs, RAG, and agentic workflows.

You will collaborate with architects, data scientists, and clients to translate AI concepts into scalable, secure applications for enterprise environments. The role emphasizes responsible AI, security, and practical deployment across cloud and on-prem environments, with a focus on Python-based

Qualifications

• Bachelor's degree or higher in CS, IT, engineering, data science, or related disciplines.
• 3+ years of professional software development or AI engineering.
• Strong hands-on Python development experience.
• Proven experience building AI-enabled solutions using LLMs/GenAI technologies.
• Experience with foundation model APIs (OpenAI, Azure OpenAI, Claude, Bedrock, Gemini, Vertex AI or equivalents).
• Knowledge of prompt engineering, context engineering, embeddings, and vector search.
• Experience designing AI assistants, copilots, or agentic workflows.
• Familiarity with Git, testing, code reviews, and Agile delivery methodologies.

Responsibilities

• Design, develop, test, and deploy AI-enabled applications using LLMs, GenAI, and intelligent automation frameworks.
• Integrate foundation models and AI services from major providers (OpenAI, Azure OpenAI, Claude, Bedrock, Gemini, Vertex AI).
• Build enterprise chatbots, RAG apps, and knowledge management systems.
• Create agentic workflows and orchestration systems.
• Ensure responsible AI with guardrails, governance, and privacy considerations.
• Collaborate with stakeholders and product teams to prototype and productionize AI capabilities.
• Package and deploy AI services using Docker and cloud platforms; support operations.

Skills

Python

LLM basics

Prompt engineering

Context engineering

Agentic workflows

Stakeholder comms

Education

Bachelor's or Master's in CS

Tools

OpenAI API

Azure OpenAI

Anthropic Claude

Google Gemini

Amazon Bedrock

Hugging Face

LangChain

Docker

Git

Job description

AI Engineer (LLM, GenAI & Agentic Systems)

The ideal candidate is an AI engineer who can build and deploy real-world LLM and GenAI solutions. Experience delivering AI applications, agentic systems, RAG solutions, and AI-assisted engineering practices will be weighted more heavily than frontend framework expertise

About the Role

We are seeking a talented AI Software Engineer (LLM, GenAI & Agentic Systems) to join our growing AI Engineering team. This role focuses on designing, building, and deploying production-grade AI solutions that leverage Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows, and AI-assisted software engineering practices.

You will work closely with solution architects, product teams, data scientists, and clients to transform AI concepts into scalable, secure, and business-ready applications. Beyond writing high-quality software, you will play a key role in shaping how AI technologies are applied responsibly and effectively across enterprise environments.

Key Responsibilities

AI Solution Development

• Design, develop, test, and deploy AI-enabled applications using Large Language Models (LLMs), Generative AI technologies, and intelligent automation frameworks.
• Integrate foundation models and AI services such as OpenAI, Azure OpenAI, Anthropic Claude, Amazon Bedrock, Google Gemini, Vertex AI, or equivalent platforms.
• Develop AI-powered solutions including:
• Enterprise chatbots and virtual assistants
• Retrieval-Augmented Generation (RAG) applications
• Intelligent document processing solutions
• Knowledge management assistants
• AI copilots and task automation agents
• Multi-agent and agentic workflow systems
• Translate business requirements into scalable AI solution architectures.
• Partner with stakeholders to prototype, demonstrate, and productionize AI capabilities.

Agentic Systems & Intelligent Automation

• Design and implement agent-based architectures and workflow orchestration solutions.
• Build systems that leverage tool/function calling, memory management, context engineering, and task planning.
• Develop AI workflows that interact with enterprise applications, APIs, and knowledge repositories.
• Evaluate and optimize agent performance for accuracy, reliability, latency, and operational efficiency.
• Implement suitable safeguards and human-in-the-loop controls for critical business processes.

Retrieval-Augmented Generation (RAG) & Knowledge Systems

• Design and implement RAG architectures using embeddings, semantic search, and retrieval pipelines.
• Develop ingestion, indexing, retrieval, and ranking workflows for enterprise knowledge repositories.
• Optimize retrieval quality, grounding accuracy, and response relevance.
• Work with vector search technologies, retrieval architectures, and semantic indexing approaches.
• Evaluate retrieval effectiveness using structured testing methodologies and performance metrics.

AI-Assisted Software Engineering

• Utilize AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, Gemini Code Assist, Amazon Q Developer, JetBrains AI Assistant, Windsurf, or equivalent tools.
• Apply prompt engineering and context management techniques to improve software development productivity.
• Leverage AI tools for:
• Code generation
• Unit test generation
• Documentation
• Refactoring
• Code reviews
• Technical analysis
• Critically review AI-generated outputs for correctness, maintainability, security, compliance, and performance.

Software Engineering & Integration

• Develop applications, services, APIs, and automation components primarily using Python and related technologies.
• Design and implement RESTful APIs and integrations with third-party platforms and enterprise systems.
• Build reusable AI services, SDKs, and internal accelerators.
• Apply software engineering best practices including version control, testing, code reviews, and documentation.
• Participate in architecture reviews and technical design discussions.

AI Quality, Evaluation & Responsible AI

• Define evaluation frameworks for AI applications and establish measurable quality criteria.
• Use AI evaluation methodologies and tools to assess:
• Accuracy
• Grounding quality
• Hallucination rates
• Latency
• User satisfaction
• Implement guardrails, monitoring, and governance controls to support responsible AI adoption.
• Ensure compliance with organisational security, privacy, data protection, and intellectual property policies.
• Promote ethical and responsible use of AI technologies.

Deployment & Operations

• Package and deploy AI applications using containerisation technologies such as Docker.
• Collaborate with DevOps teams to deploy AI services into cloud and enterprise environments.
• Implement application logging, monitoring, observability, and AI performance telemetry.
• Support production deployments and troubleshoot operational issues.

Collaboration & Knowledge Sharing

• Work closely with business stakeholders, product managers, architects, and data scientists.
• Contribute to solution design workshops, proofs of concept, and client demonstrations.
• Share best practices and mentor team members on AI engineering methodologies.
• Create reusable assets, templates, reference implementations, and technical documentation.

Required Qualifications

• Bachelor's Degree, Master's Degree or equivalent practical experience in Computer Science, Information Technology, Engineering, Data Science, or related disciplines.
• 3+ years of professional software development, AI engineering, automation engineering, or related experience.
• Strong hands‑on Python development experience.
• Demonstrated experience building and deploying AI-enabled solutions using LLMs or Generative AI technologies.
• Experience integrating foundation model APIs such as:
• OpenAI
• Azure OpenAI
• Anthropic Claude
• Google Gemini
• Amazon Bedrock
• Hugging Face
• Equivalent platforms
• Strong understanding of:
• Prompt engineering
• Context engineering
• LLM fundamentals
• Embeddings
• Vector search
• Retrieval-Augmented Generation (RAG)
• Experience designing and implementing AI assistants, copilots, chatbots, agentic workflows, or intelligent automation solutions.
• Familiarity with software engineering best practices including Git, code reviews, testing, and Agile delivery methodologies.
• Strong problem-solving and analytical thinking skills.
• Excellent communication and stakeholder engagement skills.

Preferred Qualifications

Candidates with one or more of the following will have an advantage:

• Experience building multi-agent systems or autonomous workflows.
• Experience with AI orchestration frameworks such as:
• LangChain
• LlamaIndex
• Semantic Kernel
• CrewAI
• AutoGen
• OpenAI Agents SDK
• PydanticAI
• Equivalent frameworks
• Experience with vector databases or retrieval platforms such as:
• Pinecone
• Weaviate
• FAISS
• pgvector
• Chroma
• Milvus
• Experience with AI evaluation frameworks such as Promptfoo, RAGAS, DeepEval, or equivalent.
• Experience deploying solutions using Docker and container technologies.
• Exposure to AWS, Azure, or GCP AI services.
• Experience with frontend technologies such as React, Angular, or modern web frameworks.
• Familiarity with CI/CD pipelines and DevOps practices.