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Truminds - AI/ML Lead - Python/LLM
Truminds - AI/ML Lead - Python/LLMTruminds Software Technologies • Gurgaon
Truminds - AI / ML Lead - Python / LLM

Truminds - AI / ML Lead - Python / LLM

Truminds Software Technologies • Gurgaon
17 days ago
Job description

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 :

1. 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.

2. 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.
  • 3. 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.
  • 4. 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.
  • 5. 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.
  • 6. 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)

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