AI/ML Engineering
Production AI built, deployed, and operated on Kubernetes across AWS, GCP, Azure, and Oracle OCI by AI/ML engineers embedded in your team.
The hard part of production AI is rarely the model. It is the gap between a working pilot and a system that serves production traffic: serving infrastructure, evaluation gates, observability, rollback, and cost control.
Sophotech places senior AI/ML engineers to close that gap. They build and operate model serving, retrieval pipelines, fine-tuned models, and agentic systems under your management and inside your toolchain, on Kubernetes or managed cloud inference, provisioned with Terraform across AWS, GCP, Azure, and Oracle OCI.
MLOps & Model Serving
Engineers build the path from a trained model to a served endpoint: model registries, experiment tracking, and automated promotion through staging into production. Serving runs on Kubernetes with KServe or Seldon, or on managed inference like SageMaker, Vertex AI, and Azure ML, depending on what the rest of your platform already uses.
Serving deployments are GitOps-managed. Every rollout and rollback is a reviewable commit, reconciled by FluxCD or ArgoCD. Sophotech engineers contribute upstream to FluxCD itself and ship code into the layer that delivers your models.
Deliverables
- Model registry with versioning, lineage, and promotion workflow
- Experiment tracking wired into training pipelines
- Kubernetes inference services with autoscaling and canary rollout
- GitOps-managed serving deployments with reviewable rollback
- Drift monitoring feeding retraining triggers
Tools: MLflow · KServe · Seldon · SageMaker · Vertex AI · Azure ML · FluxCD · ArgoCD
LLM & RAG Engineering
Chunking strategy, embedding model selection, index design, and query rewriting often matter more than the choice of LLM. Engineers build these pipelines in Python and Go: ingestion, embedding, vector search on pgvector, OpenSearch, Qdrant, or Weaviate, and reranking where it improves results enough to justify the latency.
Guardrails and evaluation harnesses are built into the pipeline from the start. They cover input and output filtering, grounding checks against retrieved context, and regression suites that run on every prompt or pipeline change.
Deliverables
- Retrieval pipeline with chunking, embedding, and reranking stages
- Vector index design matched to query patterns
- Guardrail layer for input and output filtering
- Evaluation harness with retrieval and answer-quality metrics
- Versioned prompt and pipeline configuration
Tools: Python · Go · pgvector · OpenSearch · Qdrant · Weaviate · LangChain
Generative AI Implementation
The work is implementation. Engineers wire generative models into existing products and workflows behind your APIs, with prompts and model versions managed in Git and evaluated before promotion. Open-weight and proprietary models are both in scope. Llama, Mistral, GPT, Claude, and Gemini get selected against your latency, cost, and data-handling constraints.
Where a model needs adapting, engineers own the pipeline. Parameter-efficient fine-tuning with LoRA and QLoRA runs as reproducible jobs, with evaluation gates wired in before anything serves. The deliverable is a working feature behind your existing APIs.
Deliverables
- Fine-tuning and evaluation pipeline producing reproducible model versions
- Quantized model variant sized to the inference budget
- Prompt and model version management in Git
- Generation features integrated behind existing product APIs
Tools: PyTorch · Hugging Face · vLLM · Ray · ONNX Runtime
Agentic AI Systems
An agent is a loop that calls tools and decides what to do next. Without controls, it fails in unbounded ways. Engineers build agentic systems as controlled software, with explicit orchestration graphs, typed tool-calling contracts, schema-validated structured outputs, and budgets on steps, tokens, and spend.
Human-in-the-loop checkpoints gate the actions that matter, such as writes, payments, and anything irreversible. Every run is traced end to end, so you can inspect what it did. Where a deterministic pipeline does the job, engineers say so and build that instead.
Deliverables
- Orchestration graph with typed tool-calling contracts
- Schema validation on every structured output
- Human approval checkpoints for irreversible actions
- End-to-end traces for every agent run
- Step, token, and spend budgets enforced at runtime
Tools: LangGraph · Pydantic · Temporal · OpenTelemetry · Langfuse
AI Platform & Infrastructure
The substrate under the models is ordinary infrastructure run well. Engineers set up GPU node pools with bin-packing and time-slicing, autoscaling that distinguishes training from inference, and quotas that keep one team from crowding out the rest. Everything is provisioned with Terraform and delivered through FluxCD or ArgoCD.
Inference cost is engineered up front through per-workload allocation, right-sized GPU classes, batching, and caching, with spend visible per model and per team.
Deliverables
- Terraform modules for GPU node pools and quotas
- Model CI/CD with registry promotion and GitOps delivery
- Inference autoscaling tuned per workload class
- Per-model and per-team cost allocation dashboards
- Capacity runbooks for training and inference fleets
Tools: Terraform · Kubernetes · Karpenter · NVIDIA GPU Operator · FluxCD · ArgoCD · Kubecost
Model Evaluation & Observability
Evaluation is treated as a first-class system. Engineers build harnesses that score model and pipeline changes before promotion, using golden datasets, LLM-as-judge where it holds up, and human review for the cases where it does not. The same checks run in CI so quality regressions block the release.
In production the same signals keep flowing. Engineers track drift against training distributions, grounding and refusal rates, latency percentiles, and cost per request, alerted through the observability stack you already run on Prometheus, Grafana, and OpenTelemetry.
Deliverables
- Evaluation harness gating promotion in CI
- Golden datasets with versioned scoring criteria
- Drift, latency, and cost dashboards per model
- Alert rules routed to owning teams
- Versioned evaluation reports per model release
Tools: Prometheus · Grafana · OpenTelemetry · MLflow · Evidently · Ragas · Langfuse
Engagements are open-ended and embedded in your team, under your management and processes, and delivery direction stays with you. Sophotech, a European company, holds the employment side, including contracts, payroll, and compliance. You interview every engineer before the engagement starts, and the engagement scales from a single engineer to a small unit as the work demands.
Explore engagement options in Talent ServicesFrequently asked questions
How does an embedded AI/ML engineer work with our team?
Under your management, inside your tooling. The engineer joins your standups, works in your repositories and CI, and follows your review process. You select the engineer through your own interviews before the engagement starts; Sophotech handles employment, contracts, and payroll in the background.
What does production-ready mean for an AI system?
Four properties make the difference: evaluation gates that score changes before they ship, observability across quality, latency, and cost, tested rollback for models and prompts, and an operational handover that gives your team the runbooks, alerts, and dashboards to run the system without us.
How do GDPR, NIS2, DORA, and data residency shape the architecture?
As engineering constraints, from the start. Data residency decides which regions models run in and where embeddings, logs, and traces are stored. GDPR shapes what enters training data and prompts. NIS2 and DORA add audit logging, access control, and documented change management. Engineers build the controls into the pipeline, together with the evidence they produce.
Who owns the models, code, and infrastructure?
You do. Code lives in your repositories, models in your registry, infrastructure in your cloud accounts. Fine-tuned weights, prompts, evaluation datasets, and Terraform state are yours from the first commit; when an engagement ends, nothing has to be migrated or bought back.
Need something not listed here? Send us your spec and we will scope a fit.
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