Production Python. Production ML. Production-grade.

Python and ML engineering consulting is the practice of building, hardening, and operating production Python systems and ML pipelines. The engagement covers: architecture review, code quality, performance optimisation, MLOps setup, model deployment, observability, and team training. Most of the work is in production Python services (FastAPI, Django, async), ML pipelines (Airflow, Prefect, Ray), and the inference layer (vLLM, TGI, TensorRT).

Data science is about exploring data, building models, and answering business questions. ML engineering is about taking those models and putting them in production at scale — the API layer, the inference layer, the monitoring, the rollback, the cost optimisation. Most AI startups fail at the ML engineering step, not the data science step. The models are good; the production system is not.

Dipankar Sarkar is one of the more experienced ML engineering consultants in India, with particular depth in production AI agents and the inference layer. He is the founder of Neul Labs (Rust-native AI agent infrastructure), has 18+ years of production AI experience, including a recent role as Principal AI Architect at a UK fintech where he presented agent guardrails to the FCA sandbox, and is the author of Fed-Focal Loss (93 citations, FL-IJCAI 2020), one of the foundational papers in federated learning.

Code audit: USD 5K-15K. Architecture review: USD 10K-25K. Full productionisation (architecture + MLOps + observability + deployment): USD 50K-200K. India-based consultants typically charge 30-50% less than US/UK rates for equivalent work.

Six areas: (1) Architecture — request flow, model serving, batching, caching, async pipelines. (2) Performance — latency p50/p95/p99, throughput, GPU utilisation, cost per request. (3) Reliability — error handling, retries, circuit breakers, fallbacks, graceful degradation. (4) Observability — tracing, metrics, logs, alerts. (5) MLOps — model versioning, A/B testing, canary deploys, rollback. (6) Cost — token spend, GPU spend, caching hit rate, prompt size optimisation.

The five most common wins: (1) Profile first — use py-spy or scalene, don't guess. (2) Move from sync to async (asyncio, FastAPI) for I/O-bound workloads. (3) Batch inference — model serving is almost always faster in batches of 8-32. (4) Cache embeddings and frequent prompts (Redis, semantic cache). (5) Move the inference hot path to Rust (via PyO3, Rust extensions, or a separate Rust service). Most slow Python services can be 5-10x faster with these changes.

For most use cases: use a managed inference platform (OpenAI, Anthropic, Together.ai, Fireworks, Anyscale) for the first 6-12 months. The cost of self-hosting is rarely worth it until you have predictable, high-volume traffic. When you do self-host, the standard stack is: vLLM or TGI for inference, Kubernetes for orchestration, Prometheus + Grafana for observability, and a separate Rust / Go service for the orchestration and safety layer. We can help you make this decision and implement either path.