Senior AI/ML Engineer

• Lead the ideation, design, and execution of AI proofs-of-concept and end-to-end machine learning systems, from research and experimentation through to scalable production deployment.
• Develop and implement machine learning and AI systems across domains including Computer Vision, NLP, and Generative AI, solving complex business and product challenges.
• Collaborate cross-functionally with engineering, product management, and business stakeholders to translate business objectives into scalable AI-driven capabilities and production-ready solutions.
• Architect and maintain production-grade AI systems across the full lifecycle, including data ingestion, feature engineering, model training, retrieval pipelines, orchestration layers, deployment, evaluation, and monitoring.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, semantic search systems, and multi-agent workflows, selecting appropriate architectures based on performance, latency, cost, and compliance constraints.
• Design evaluation frameworks for machine learning and generative AI systems, including offline evaluation, human-in-the-loop validation, automated scoring, and production monitoring.
• Optimize AI systems for scalability, latency, and cost efficiency, including batching, caching, quantization, and model selection strategies.
• Design and apply advanced mathematical models and statistical methods to analyze large, multi-modal datasets, including time series, classification, regression, and representation learning tasks.
• Collaborate on AI architecture decisions and contribute to technical roadmap planning, identifying high-impact opportunities for AI adoption across the organization.
• Mentor and provide technical guidance to junior ML engineers and data scientists, promoting engineering best practices and a culture of continuous learning.
• Stay current with emerging research, tools, and architectures in AI and machine learning, proactively evaluating and introducing relevant advancements.

Required skills:

• 5+ years of hands-on experience in machine learning, AI research, or data science, with a proven track record of delivering production-grade ML solutions.
• Master’s or Ph.D. in Computer Science, Data Science, Machine Learning, or a related quantitative field.
• Strong expertise across core ML disciplines including supervised and unsupervised learning, time series modeling, classification, regression, and model evaluation.
• Deep hands-on experience developing production systems in Computer Vision, NLP, and Generative AI.
• Strong experience building LLM-based applications, including RAG pipelines, agentic workflows, semantic search, and tool-using AI systems.
• Experience designing evaluation frameworks for generative AI systems, including prompt evaluation, hallucination detection, and performance monitoring.
• Proficiency in Python and modern ML ecosystem including PyTorch, TensorFlow, Hugging Face, NumPy, Pandas, and scikit-learn.
• Experience with modern LLM orchestration and serving frameworks such as LangChain, LlamaIndex, DSPy, Haystack, or equivalent.
• Experience deploying and scaling AI systems on cloud platforms (AWS, Azure, or GCP), including managed ML services and containerized deployments.
• Experience building and maintaining end-to-end ML pipelines, including data processing, model training, deployment, and monitoring.
• Understanding of responsible AI principles, including bias mitigation, safety guardrails, and governance considerations.
• Proficiency with version control (Git) and software engineering best practices for collaborative ML development.
• Excellent communication skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders.

Will be a plus:

• Experience with MLOps tooling and workflows, including MLflow, Apache Airflow, Terraform, and CI/CD pipelines for model deployment.
• Experience with model serving and inference optimization tools such as vLLM.
• Experience working with feature stores, real-time data pipelines, or streaming AI systems.
• Experience with distributed training or large-scale model training frameworks.
• Experience with multimodal AI systems (vision-language models, audio-text, document AI).
• Experience with fine-tuning large language models or parameter-efficient training techniques (LoRA, PEFT, etc.).
• Experience contributing to open-source ML projects or publications in peer-reviewed venues.