AI Engineering Lead

What is this position about

• Lead end-to-end project delivery with clear governance and strong stakeholder communication
• Mentor junior engineers and contribute to proposals and new business initiatives
• Define what AI systems should and should not attempt and communicate risks and tradeoffs transparently to clients
• Design and build RAG systems agentic frameworks and LLM-powered solutions robust enough for production
• Apply advanced prompt engineering techniques including instruction design few-shot sets structured outputs and tool/agent prompts
• Lead feasibility assessments to select the right approach among prompting RAG fine-tuning or classical ML
• Design evaluation frameworks including LLM-as-a-judge methods custom metrics ( ) and go/no-go gates
• Run structured experiments across prompts retrievers chunking strategies and models grounded in evidence rather than intuition
• Identify and categorize model failure modes including hallucinations retrieval misses and instruction-following errors
• Build scalable inference infrastructure and CI/CD pipelines for AI/ML models
• Automate the full MLOps/LLMOps lifecycle including tracking versioning deployment monitoring and retraining
• Design APIs microservices and orchestration layers optimized for latency cost and reliability

Qualifications :

• Expert-level Python strong Git practices and experience with ML/LLM versioning
• Solid cloud experience across AWS Azure or GCP (Azure preferred) plus containerization and orchestration
• Hands-on RAG experience covering chunking embeddings retrieval reranking and evaluation
• Proven MLOps/LLMOps track record using tools such as MLflow Weights & Biases or similar
• Practical evaluation design skills including metrics dataset curation and structured experimentation
• Experience with event-driven architectures APIs and microservices
• Strong communication skills equally comfortable engaging engineering teams and senior stakeholders
• Preferred: experience with the Databricks MLOps platform LLM fine-tuning building agentic GenAI systems Infrastructure as Code security and observability for AI services a classical ML background and open-source contributions

What about languages

English: Advanced (required for effective communication with global teams)

How much experience must I have

6 years of experience building and deploying AI solutions in production environments with a strong track record across RAG agentic systems and MLOps/LLMOps.

Additional Information :

Our perks and benefits:

Learning Opportunities:

• Certifications in AWS (we are AWS Partners) Databricks and Snowflake.
• Access to AI learning paths to stay up to date with the latest technologies.
• Study plans courses and additional certifications tailored to your role.
• Access to Udemy Business offering thousands of courses to boost your technical and soft skills.
• English lessons to support your professional communication.

Travel opportunities to attend industry conferences and meet clients.

Mentoring and Development:

• Career development plans and mentorship programs to help shape your path.

Celebrations & Support:

• Special day rewards to celebrate birthdays work anniversaries and other personal milestones.
• Company-provided equipment.

Flexible working options to help you strike the right balance.

Other benefits may vary according to your location in LATAM. For detailed information regarding the benefits applicable to your specific location please consult with one of our recruiters.

Remote Work :

Yes

Employment Type :

Full-time