AI Engineer

• Participate in the design, development, and deployment of AI/ML solutions, from experimentation and prototyping through stable production operation.

• Collaborate with relevant stakeholders to define business problems and identify the most appropriate AI approaches based on business requirements.

• Evaluate and select suitable model architectures, frameworks, and infrastructure based on performance, cost, and latency requirements.

• Build, maintain, and continuously improve production ML pipelines, including ML CI/CD, retraining, model versioning, and monitoring.

• Ensure the quality, reliability, performance, and scalability of AI systems running in production.

• Provide technical direction, conduct design/code reviews, and mentor junior and mid-level AI/ML engineers within the team.

• Keep up with emerging AI technologies, evaluate their potential, and recommend suitable applications for products and business use cases.

• Produce technical documentation, share knowledge, and establish engineering best practices across the team.

Technical Requirements

• Proven experience delivering end-to-end AI/ML solutions to production, covering the full lifecycle from data → training/fine-tuning → serving → monitoring.

• Strong capability in fine-tuning and adapting AI/ML models to real-world conditions where base models may not fully meet requirements, using techniques such as full fine-tuning, LoRA/PEFT, RAG, prompt engineering, or equivalent approaches.

• Hands-on experience delivering real-world projects involving NLP, Computer Vision, LLMs, and AI Agents.

• Strong experience implementing RAG (Retrieval-Augmented Generation), chatbots, and Agentic AI systems for practical use cases such as Q&A, search, and recommendation.

• Experience in hardware acceleration and inference optimization, using at least one technology or tool such as CUDA, NPU, OpenCL, AVX-512, TensorRT, or TVM.

• Strong hands-on engineering capability, demonstrated through direct involvement in solution architecture, coding, implementation, and deployment in real-world projects.

• Solid MLOps experience, particularly in productionizing models and maintaining reliable AI/ML systems in production environments.

• Strong foundation in Data Structures & Algorithms, with solid system analysis and System Design capabilities to build AI solutions with clear architecture, high performance, and scalability.

General Requirements

• 5+ years of experience as an AI/ML Engineer, including hands-on experience deploying at least one AI/ML system into production.

• Proficiency in Python and at least one deep learning framework such as PyTorch or TensorFlow.

• Experience with C/C++ or other programming languages relevant to AI engineering is highly desirable.

• Strong understanding of MLOps, including Docker, ML CI/CD, model versioning, and monitoring.

Nice-to-have

• Experience with Kubernetes, Triton Inference Server, and/or vLLM.

• Experience with LLM agent orchestration tools such as LangChain, LangGraph, or internally developed agent frameworks.

• Experience with vector search and vector databases, such as FAISS, Milvus, Qdrant, Pinecone, Weaviate, or similar technologies.

• Familiarity with major cloud platforms such as AWS, GCP, or Azure, as well as their AI/ML services.

• Experience solving real-world optimization problems using mathematical optimization or metaheuristic algorithms.

• Experience designing and building custom training datasets tailored to specific business problems when existing datasets are insufficient.

• Experience deploying machine learning solutions for tabular data into production, including:

  • Time-series forecasting

  • Classification and regression

  • Business applications such as demand forecasting, spare-parts inventory optimization, and predictive maintenance.