About The Role
We are seeking an experienced AI/ML Lead to drive the design, development, deployment, and optimization of AI systems across our product ecosystem.This role requires deep expertise in machine learning (ML), large language models (LLMs), and production-grade AI engineering.As an AI/ML Lead, you will work closely with cross-functional teams to develop scalable, high-performance AI solutions that power real-time mobile and web applications.You will also take ownership of MLOps workflows, model lifecycle management, and AI infrastructure to ensure seamless end-to-end delivery.
Key Responsibilities
- AI Solution Architecture & Development :
- Architect and implement end-to-end AI solutions, including computer vision, NLP, recommendation systems, and other ML-driven use cases.
- Integrate AI capabilities into production-grade web and mobile applications, ensuring performance, reliability, and security.
- Collaborate with engineering teams to design APIs, pipelines, and system architectures for AI-enabled features.
- Large Language Model (LLM) Engineering :
- Train, fine-tune, and optimize LLMs and deep learning models for specific tasks and domains.
- Apply model compression techniques such as quantization, pruning, distillation, and hardware-specific optimizations (GPU/TPU/Edge).
- Design evaluation frameworks to benchmark model performance, latency, throughput, and accuracy.
- MLOps & Production Deployment :
- Establish and maintain MLOps pipelines, enabling automated training, validation, deployment, and monitoring of models.
- Implement best practices for model versioning, A/B testing, CI/CD integrations, and production rollout strategies.
- Monitor model performance, data drifts, and system health in production environments.
- Scalability, Performance & Infrastructure :
- Build and scale AI systems capable of handling high-volume, low-latency, real-time workloads.
- Work with cloud-native platforms (AWS, Azure, GCP) or Edge-AI frameworks to optimize deployment.
- Optimize hardware utilization across GPUs, accelerators, and distributed environments.
- Data Engineering & Feature Development :
- Collaborate with data teams to design and manage data pipelines, data preprocessing workflows, and feature engineering strategies.
- Ensure training and inference datasets are well-structured, accurate, and compliant with data governance practices.
- Implement automated monitoring for data quality, data drift, and data lineage.
- Team Leadership & Collaboration :
- Provide technical leadership, mentoring, and guidance to AI/ML engineers and cross-functional teams.
- Participate in product planning, design reviews, and roadmap discussions to align AI capabilities with business goals.
- Stay updated with emerging research, frameworks, and tools in AI/ML and share insights with the team.
Required Skills & Qualifications
- 7-10 years of hands-on experience in machine learning, deep learning, or AI product development.
- Strong expertise in training, fine-tuning, and deploying LLMs and DL models.
Deep Knowledge Of Model Optimization Techniques
- Quantization
- Pruning
- Distillation
- Hardware acceleration (GPU/TPU/Edge)
- Proficiency in building production-grade AI applications using:
- NLP frameworks, computer vision frameworks, and recommendation algorithms
- Strong programming skills in Python and ML/AI frameworks (PyTorch, TensorFlow, JAX).
- Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, etc.).
- Understanding of scalable architectures, microservices, and cloud-native Skills :
- Experience with real-time AI inference on edge devices.
- Familiarity with vector databases, embeddings, and retrieval systems.
- Experience with model evaluation tools and metrics for LLMs, CV, NLP, and multimodal models.
Prior leadership experience managing technical teams.
(ref:hirist.tech)
