AI - Machine Learning Engineer

ConsultNet is a national provider of technology talent and solutions. The Machine Learning Engineer will productize, optimize, and operate machine learning models and services at scale, focusing on distributed training, efficient inference, model serving, performance, reliability, and cost optimization. The role partners with Research, ML Ops, Platform/Infra, Data, and Product teams throughout the ML lifecycle.

Responsibilities:

• Productize and optimize models from Research into reliable, performant, and cost-efficient services with clear SLOs (latency, availability, cost)
• Scale training across nodes/GPUs (DDP/FSDP/ZeRO, pipeline/tensor parallelism) and own throughput/time-to-train using profiling and optimization
• Implement model-efficiency techniques (quantization, distillation, pruning, KV-cache, Flash Attention) for training and inference without materially degrading quality
• Build and maintain model-serving systems (vLLM/Triton/TGI/ONNX/TensorRT/AITemplate) with batching, streaming, caching, and memory management
• Integrate with vector/feature stores and data pipelines (FAISS/Milvus/Pinecone/pgvector; Parquet/Delta) as needed for production
• Define and track performance and cost KPIs; run continuous improvement loops and capacity planning
• Partner with ML Ops on CI/CD, telemetry/observability, model registries; partner with Scientists on reproducible handoffs and evaluations

Requirements:

• Bachelors in computer science, Electrical/Computer Engineering, or a related field required
• Strong systems/ML engineering with exposure to distributed training and inference optimization
• 3–5 years in ML/AI engineering roles owning training and/or serving in production at scale
• Demonstrated success delivering high-throughput, low-latency ML services with reliability and cost improvements
• Experience collaborating across Research, Platform/Infra, Data, and Product functions
• Familiarity with deep learning frameworks: PyTorch (primary), TensorFlow
• Exposure to large model training techniques (DDP, FSDP, ZeRO, pipeline/tensor parallelism)
• experience profiling and optimizing code execution and model inference: (PTQ/QAT/AWQ/GPTQ), pruning, distillation, KV-cache optimization, Flash Attention
• Scalable serving: autoscaling, load balancing, streaming, batching, caching; collaboration with platform engineers
• Data & storage: SQL/NoSQL, vector stores (FAISS/Milvus/Pinecone/pgvector), Parquet/Delta, object stores
• Write performant, maintainable code
• Understanding of the full ML lifecycle: data collection, model training, deployment, inference, optimization, and evaluation
• Master's
• distributed training experience a plus