Machine Learning Engineer

About Company:-

Our client is a Palo Alto–based AI infrastructure and talent platform founded in 2018. It helps companies connect with remote software developers using AI-powered vetting and matching technology. Originally branded as the “Intelligent Talent Cloud,”enabled companies to “spin up their engineering dream team in the cloud” by sourcing and managing vetted global talent.

In recent years, they have evolved to support AI infrastructure and AGI workflows, offering services in model training, fine-tuning, and deployment—powered by their internal AI platform, ALAN, and backed by a vast talent network. They reported $300 million in revenue and reached profitability. Their growth is driven by demand for annotated training data from AI labs, including major clients like OpenAI, Google, Anthropic, and Meta.

Job Title: Python +Machine Learning,Finetuning,CV

Location: Pan India

Experience: 9+ yrs.

Job Type : Contract to hire.

Notice Period:- Immediate joiners.

Roles & Responsibilities

• Lead and mentor a cross-functional team of ML engineers, data scientists, and MLOps professionals.
• Oversee the full lifecycle of LLM and ML projects — from data collection to training, evaluation, and deployment.
• Collaborate with Research, Product, and Infrastructure teams to define goals, milestones, and success metrics.
• Provide technical direction on large-scale model training, fine-tuning, and distributed systems design.
• Implement best practices in MLOps, model governance, experiment tracking, and CI/CD for ML.
• Manage compute resources, budgets, and ensure compliance with data security and responsible AI standards.
• Communicate progress, risks, and results to stakeholders and executives effectively.

Required Skills & Qualifications

• 9+ yrs of strong background in Machine Learning, NLP, and modern deep learning architectures (Transformers, LLMs).
• Hands-on experience with frameworks such as PyTorch, TensorFlow, Hugging Face, or DeepSpeed
• 2+ yrs of proven experience managing teams delivering ML/LLM models in production environments.
• Knowledge of distributed training, GPU/TPU optimization, and cloud platforms (AWS, GCP, Azure).
• Familiarity with MLOps tools like MLflow, Kubeflow, or Vertex AI for scalable ML pipelines.
• Excellent leadership, communication, and cross-functional collaboration skills.
• Bachelor’s or Master’s in Computer Science, Engineering, or related field (PhD preferred).

Nice to Have

• Experience training or fine-tuning foundation models.
• Contributions to open-source ML or LLM frameworks.
• Understanding of Responsible AI, bias mitigation, and model interpretability.