Role summary
We are looking for a hands-on Lead AI Engineer with 5–7 years of experience to lead the architecture, development, and production deployment of our AI and machine learning systems. You will bridge the gap between complex business challenges and scalable technical execution, spanning Generative AI/LLMs, deep learning, and classical machine learning. In this role, you will serve as both a technical architect and a hands-on builder: making high-stakes design decisions, establishing rigorous MLOps and evaluation standards, and mentoring a team of engineers to ship reliable, cost-effective AI solutions in the cloud.
Key responsibilities
End-to-End AI Delivery
Architect, train, evaluate, and deploy production-grade machine learning, deep learning (NLP, Computer Vision), and Generative AI solutions tailored to core business problems.
Build Production LLM & Agentic Systems: Design and implement scalable LLM applications utilizing advanced RAG pipelines, embeddings, structured outputs, tool calling, and multi-agent orchestration frameworks.
Drive Model & Architecture Selection: Evaluate trade-offs between prompting frontier LLMs, fine-tuning open-weights models, or training custom ML/DL models based on accuracy, latency, data privacy, and unit economics.
Establish Evaluation & Guardrails: Build automated benchmarking, validation, observability, and guardrail frameworks to monitor model drift, mitigate hallucinations, and guarantee production quality.
Cloud, Backend & MLOps Architect
Architect Scalable AI Backends: Build high-concurrency, asynchronous model-serving APIs and backend microservices using Python, FastAPI, and asyncio that integrate seamlessly with enterprise systems and databases.
Own the MLOps Lifecycle: Implement robust CI/CD pipelines, experiment tracking, model registries, automated retraining, and zero-downtime rollback strategies across cloud environments (AWS, Azure, or GCP).
Optimize Performance & Cost: Tune inference latency, throughput, and cloud infrastructure utilization through caching, batching, quantization, and scalable container orchestration.
Enforce Production Standards: Ensure all AI services adhere to strict engineering standards for security, data governance, observability, and fault tolerance.
Technical Leadership
Own Technical Architecture: Translate ambiguous product and business requirements into clear technical specifications, system architectures, and phased delivery roadmaps.
Elevate Engineering Quality: Lead architecture and code reviews, enforce software engineering best practices, and eliminate technical debt across the AI stack.
Mentor & Collaborate: Coach junior and mid-level engineers, foster a culture of pragmatic experimentation, and partner closely with Product, Data, and Platform teams to deliver measurable business impact.
Must-have skills
Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related technical field.
5–7 years of professional experience building and deploying production AI/ML, deep learning, or applied data science systems.
Expert-level Python programming skills, including object-oriented design, asynchronous programming, and building production REST APIs with FastAPI.
Strong foundation in machine learning fundamentals, statistical modeling, feature engineering, and deep learning frameworks (PyTorch or TensorFlow).
Proven hands-on experience building and shipping Generative AI applications, including LLM integrations, RAG architectures, vector search, and prompt/context engineering.
Production experience with at least one major cloud provider (AWS, Azure, or GCP), alongside containerization (Docker) and CI/CD workflows.
Solid proficiency in SQL, relational and NoSQL/vector databases, and handling large-scale data pipelines.
Demonstrated technical leadership experience, including owning system architecture, leading cross-functional technical initiatives, and mentoring engineers.
Good-to-have skills
Hands-on experience self-hosting, serving (e.g., vLLM, TGI, Triton), and fine-tuning (LoRA/QLoRA, PEFT) open-weights LLMs.
Specialized production experience in either Computer Vision (object detection, segmentation, OCR) or Advanced NLP (custom transformers, information extraction, semantic search).
Proficiency with LLM orchestration and evaluation ecosystems (e.g., LangGraph, LlamaIndex, LangSmith, Ragas) and cloud AI platforms (AWS Bedrock/SageMaker, Azure AI, or Vertex AI).
Experience with Kubernetes, distributed computing (Spark/Ray), and end-to-end MLOps tooling (MLflow, Weights & Biases, Kubeflow).
Experience
5-7
Work mode
Onsite