Senior AI Engineer
Techsa
- Location
- Remote
- Employment
- Full-time
- Level
- Senior Level
Posted 2 days ago
About the Role
Techsa is a software development firm accelerating digital transformation through IoT, data management, and AI/ML solutions. This role involves owning the AI layer, including retrieval, agents, and natural language interfaces over enterprise data to deliver scalable, production-ready solutions.
Skills
LLM Application Engineering
RAG
Retrieval Architecture
Embeddings
Vector Search
Agent Systems
Tool Calling
LLM Evaluation
Python
Data Engineering
Software Engineering
vLLM
Inference Optimization
GPU Resource Management
Fine Tuning
Text to SQL
Perks
- Remote Work
Full job details
This is a remote position.
We are looking for a Senior AI Engineer to join our team and take ownership of key areas within our technology and data platform. Owns the AI layer: retrieval, agents, and natural language interfaces over enterprise data, with evaluation so quality is measured rather than demonstrated.
Key Responsibilities
• Own and deliver solutions within the scope of the role, from requirements and technical/design decisions through implementation and continuous improvement.
• Work closely with engineering, product, data, design, and business stakeholders to translate requirements into practical, scalable solutions.
• Apply strong engineering and/or domain expertise to build reliable, maintainable, and production-ready capabilities.
• Contribute to architecture, standards, documentation, quality, and technical decision-making appropriate to the role.
• Identify performance, scalability, data quality, usability, reliability, or operational risks and address them proactively.
• Collaborate across teams to ensure solutions integrate effectively with existing systems and platform components.
Requirements
• Experience: 5+ years of relevant professional experience.
• Strong hands-on experience with: LLM application engineering, RAG and retrieval architecture, embeddings and vector search, agent and tool calling systems, LLM evaluation, Python.
• Software or data engineering background, with at least two years building LLM based systems that reached production.
• Retrieval architecture: chunking, embeddings, vector and hybrid search, and why naive RAG fails on structured data.
• Agent or tool calling systems, including permissions, scoping, and guardrails.
• Evaluation practice: test sets, regression suites, hallucination and grounding checks.
• Comfortable working inside a data platform rather than calling a hosted API.
Preferred Qualifications
• Self hosted open weight model serving with vLLM or equivalent, inference optimisation, and GPU resource management.
• Fine tuning or adaptation of open weight models.
• Text to SQL or semantic layer work over a real data model.