AI / ML Engineer

About the role

We are looking for an AI/ML Engineer to help build and ship the AI systems at the core of v2softech's product portfolio — spanning document intelligence, operational assistants, voice AI, and enterprise knowledge platforms. You will work primarily with open-source models, fine-tuning them for specific domains, integrating them into production systems, and ensuring they perform reliably in real operational environments. This is applied AI engineering, not research.

What you will do

• Select, evaluate, and deploy open-source language, vision, and voice models appropriate to each product's requirements
• Design and implement fine-tuning pipelines to adapt models to domain-specific data (finance, healthcare, operations)
• Build retrieval-augmented generation (RAG) systems that ground model outputs in accurate, up-to-date organisational knowledge
• Integrate AI models into production APIs and product workflows with appropriate latency, reliability, and error-handling standards
• Evaluate model performance rigorously — not just on benchmarks, but on real operational tasks that matter to our clients
• Implement responsible AI practices including output validation, guardrails, and audit logging
• Work closely with the engineering team to ensure AI components are operationally sound, not just technically impressive
• Stay current with the open-source AI ecosystem and identify emerging models and techniques relevant to our product direction

What we are looking for

• 3+ years of experience building and deploying machine learning or AI systems in production
• Hands-on experience with open-source LLMs (Llama, Mistral, Phi, Gemma, or similar)
• Proficiency in Python and the ML ecosystem: PyTorch, Hugging Face Transformers, LangChain or LlamaIndex
• Experience with fine-tuning techniques including LoRA, QLoRA, or full fine-tuning on domain-specific datasets
• Understanding of vector databases and embedding-based retrieval (Pinecone, Qdrant, pgvector, or similar)
• Ability to evaluate model outputs critically and design evaluation frameworks for real-world task performance
• Strong communication skills — able to explain AI system behaviour to non-technical stakeholders clearly

Nice to have

• Experience with voice AI, speech recognition, or voice synthesis systems
• Background in NLP with document-heavy workloads (extraction, classification, summarisation)
• Experience running inference on-premise or on self-managed GPU infrastructure
• Familiarity with data governance requirements in regulated industries
• Contributions to open-source AI projects or published research