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AI / ML Solution Architect

AI / ML Solution Architect

Fission Labshyderabad, telangana, in
20 hours ago
Job description

Role : Solution Architect-AI / ML

Experience : 10+ years

Location : Hyderabad & Pune

Key Responsibilities :

Architecture & Infrastructure

  • 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.

Performance & Optimization

  • Conduct performance benchmarking, hardware comparisons, and cost-performance trade-off analysis.
  • Implement real-time monitoring and control systems with metrics collection, observability, and custom performance tracking.
  • Optimize cost models, budget predictability, and resource utilization.

    • Data & Training Pipelines

  • Architect and validate data pipelines with storage, persistence, and throughput optimization.
  • Oversee data quality validation, pre-processing, and long-term experiment tracking.
  • Support framework flexibility for diverse training techniques (supervised, unsupervised, fine-tuning, reinforcement learning).

    • Integration & Deployment

  • Ensure seamless deployment across multi-cloud environments with security, compliance, and regional availability considerations.
  • Collaborate with DevOps and MLOps teams for automation, fault tolerance, job scheduling, and orchestration testing.
  • Provide technical guidance on integration with existing enterprise systems.

    • Analysis & Recommendations

  • Lead result analysis, insight generation, and actionable recommendations for training performance and user experience improvements.
  • Present performance claims, benchmarking reports, and speculative decoding insights to stakeholders.

    • Technical Expertise Requirements

  • 10+ years in architecture roles with at least 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).

    • Performance & Cost Management

  • Demonstrated expertise in performance benchmarking, reliability testing, and training speed optimization.
  • Skilled in cost modeling, budget forecasting, and cost-performance balancing.

    • Monitoring & Observability

  • Experience with real-time monitoring tools (Prometheus, Grafana, CloudWatch) and custom metric instrumentation.
  • Familiarity with network performance testing, regional load testing, and multi-region deployment strategies.

    • Soft Skills

  • Strong problem-solving skills with an analytical mindset.
  • Excellent communication skills to present technical trade-offs and strategic recommendations to executives and engineering teams.
  • Ability to lead cross-functional teams and drive innovation in AI infrastructure.

    • Other Required Skills :

    LLM Inference Optimization

  • 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.

    • Inference Frameworks

  • 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).

    • Performance Engineering

  • 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).

    • System Architecture

  • Design scalable inference systems meeting strict latency SLAs and throughput requirements.
  • Implement production patterns for request routing, load balancing, and model versioning.
  • Balance trade-offs between latency, throughput, cost per token, and model accuracy.
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    Solution Architect • hyderabad, telangana, in

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