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Fission Labs Logo Fission Labs · IT - Software Development

Lead AI/ML Engineer

India, Hyderabad & Pune · Job Posted March 04, 2026
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Job Responsibility

  • Design, implement, and optimize end-to-end ML training workflows including infrastructure setup, orchestration, fine-tuning, deployment, and monitoring
  • Evaluate and integrate multi-cloud and single-cloud training options across AWS and other major platforms
  • Lead cluster configuration, orchestration design, environment customization, and scaling strategies
  • Compare and recommend hardware options (GPUs, TPUs, accelerators) based on performance, cost, and availability

Requirements

  • Experience with cloud-based platforms (AWS, Azure), API integrations, and data models
  • Exposure to AI/ML-enabled platforms or decision-intelligence systems
  • Certifications: CBAP / PMI-PBA / Agile BA / SAFe Product Owner / Scrum Master
  • Experience in stakeholder training, change management, or workshop facilitation
  • At least 4-5 years in AI/ML infrastructure and large-scale training environments
  • Expert in AWS cloud services (EC2, S3, EKS, SageMaker, Batch, FSx, etc.) and familiar with Azure, GCP, and hybrid/multi-cloud setups
  • Strong knowledge of AI/ML training frameworks (PyTorch, TensorFlow, Hugging Face, DeepSpeed, Megatron, Ray, etc.)
  • Proven experience with cluster orchestration tools (Kubernetes, Slurm, Ray, SageMaker, Kubeflow)
  • Deep understanding of hardware architectures for AI workloads (NVIDIA, AMD, Intel Habana, TPU)
  • Expert knowledge of inference optimization techniques including speculative decoding, KV cache optimization (MQA/GQA/PagedAttention), and dynamic batching
  • Deep understanding of prefill vs decode phases, memory-bound vs compute-bound operations
  • Experience with quantization methods (INT4/INT8, GPTQ, AWQ) and model parallelism strategies
  • Hands-on experience with production inference engines: vLLM, TensorRT-LLM, DeepSpeed-Inference, or TGI
  • Proficiency with serving frameworks: Triton Inference Server, KServe, or Ray Serve
  • Familiarity with kernel optimization libraries (FlashAttention, xFormers)
  • Proven ability to optimize inference metrics: TTFT (first token latency), ITL (inter-token latency), and throughput
  • Experience profiling and resolving GPU memory bottlenecks and OOM issues
  • Knowledge of hardware-specific optimizations for modern GPU architectures (A100/H100)
  • Drive end-to-end fine-tuning of LLMs, including model selection, dataset preparation/cleaning, tokenization, and evaluation with baseline metrics
  • Configure and execute fine-tuning experiments (LoRA, QLoRA, etc.) on large-scale compute setups, ensuring optimal hyperparameter tuning, logging, and checkpointing
  • Document fine-tuning outcomes by capturing performance metrics (losses, BERT/ROUGE scores, training time, resource utilization) and benchmark against baseline models

What we offer

  • Opportunities for continuous learning and certification support
  • Collaborative and growth-oriented work culture
  • Competitive compensation and comprehensive benefits
  • Exposure to modern cloud and integration technologies
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