AI Lead

At Codvo, software and people transformations go hand-in-hand. We are a global empathy-led technology services company where product innovation and mature software engineering are embedded in our core DNA. Our core values of Respect, Fairness, Growth, Agility, and Inclusiveness guide everything we do. We continually expand our expertise in digital strategy, design, architecture, and product management to offer measurable results and outside-the-box thinking.

We are seeking a highly skilled and experienced Senior AI Engineer with a deep understanding of asset-heavy industries such as oil & gas, manufacturing, or utilities to lead the design, development, and implementation of robust and scalable pipelines and backend systems for our Generative AI applications. In this role, you will be responsible for orchestrating the flow of data, integrating AI services, developing RAG pipelines, working with LLMs, and ensuring the smooth operation of the backend infrastructure that powers our Generative AI solutions.

You will also be expected to apply modern LLMOps practices, handle schema-constrained generation, optimize cost and latency trade-offs, mitigate hallucinations, and ensure robust safety, personalization, and observability across GenAI systems.

• Design and implement scalable and modular pipelines for data ingestion, transformation, and orchestration across GenAI workloads.
• Manage data and model flow across LLMs, embedding services, vector stores, SQL sources, and APIs.
• Build CI/CD pipelines with integrated prompt regression testing and version control.
• Use orchestration frameworks like Lang Chain or Lang Graph for tool routing and multi-hop workflows.
• Monitor system performance using tools like Langfuse or Prometheus.

• Develop systems to ingest unstructured (PDF, OCR) and structured (SQL, APIs) data.
• Apply preprocessing pipelines for text, images, and code.
• Ensure data integrity, format consistency, and security across sources.

• Integrate external and internal LLM APIs (OpenAI, Claude, Mistral, Qwen, etc.).
• Build internal APIs for smooth backend-AI communication.
• Optimize performance through fallback routing to classical or smaller models based on latency or cost budgets.
• Use schema-constrained prompting and output filters to suppress hallucinations and maintain factual accuracy.

• Build hybrid RAG pipelines using vector similarity (FAISS/Qdrant) and structured data (SQL/API).
• Design custom retrieval strategies for multi-modal or multi-source documents.
• Apply post-retrieval ranking using DPO or feedback-based techniques.
• Improve contextual relevance through re-ranking, chunk merging, and scoring logic.

• Manage prompt engineering, model interaction, and tuning workflows.
• Implement LLMOps best practices: prompt versioning, output validation, caching (KV store), and fallback design.
• Optimize generation using temperature tuning, token limits, and speculative decoding.
• Integrate observability and cost-monitoring into LLM workflows.

• Design and maintain scalable backend services supporting GenAI applications.
• Implement monitoring, logging, and performance tracing.
• Build RBAC (Role-Based Access Control) and multi-tenant personalization.
• Support containerization (Docker, Kubernetes) and autoscaling infrastructure for production.

• Bachelor’s or Master’s in Computer Science, Artificial Intelligence, Machine Learning, or related field.

• 5+ years of experience in AI/ML engineering with end-to-end pipeline development.
• Hands-on experience building and deploying LLM/RAG systems in production.
  
  Strong experience with public cloud platforms (AWS, Azure, or GCP).
• 2+ years of experience in asset-heavy industries (e.g., oil & gas, utilities, manufacturing).

• Proficient in Python and libraries such as Transformers, Sentence Transformers, PyTorch.
• Deep understanding of GenAI infrastructure, LLM APIs, and tool chains like Lang Chain/Lang Graph.
• Experience with RESTful API…