Lead Engineer - MLOps & Generative AI

Role Overview :

We are seeking a Lead Machine Learning & MLOps Systems Architect to own the end-to-end lifecycle of our enterprise AI solutions. In this role, you will bridge the gap between advanced data science research and robust software engineering, designing scalable architectures that support computer vision, perception, and complex GenAI orchestration (LLMs + Vector DBs + OCR). You will champion automation through CI/CD/CT pipelines, ensuring our production systems are optimized for latency, cost, throughput, and hardware efficiency.

Key Responsibilities :

- End-to-End ML Architecture: Design and deploy production-grade ML system architectures featuring asynchronous processing queues, microservices, and optimized CPU/GPU deployment strategies.

- GenAI & Multimodal Orchestration: Build pipelines integrating LLMs, Document Processing (OCR), embedding layers, and custom prompt/retrieval systems (RAG).

- MLOps & Pipeline Automation: Automate the end-to-end model lifecycle using CI/CD/CT (Continuous Training) frameworks, model versioning, and rigorous traceability across environments.

- System Scaling & Optimization: Maximize system throughput and minimize latency by implementing model selection, distillation, and quantization strategies (e.g., ONNX, GGUF).

- Production Governance: Implement comprehensive model monitoring systems to track data/concept drift, latency spikes, and infrastructure costs in real time.

Required Qualifications & Technical Stack :

- Experience : 10+ years in the IT industry with 8+ years of core hands-on software development and machine learning systems experience.

- Core Languages & Frameworks : Expert-level Python proficiency along with Deep Learning frameworks.

- GenAI Ecosystem : Proven experience with LLMs, Vector Databases, prompt pipelines, and structured data extraction frameworks.

- MLOps & Infrastructure : Advanced experience with CI/CD tools (GitHub Actions, Jenkins) applied to ML workflows.

- Cloud Platforms : Hands-on experience with cloud-native AI suites (such as AWS SageMaker, GCP Vertex AI, or Azure/Bedrock ecosystem).

- Education : Bachelors or Masters degree in Computer Science, Data Science, or a related quantitative field.

(ref:hirist.tech)