Generative AI + RAG
Retrieval-augmented generation with proper chunking, hybrid search, reranking, and evals. Vector stores chosen against the workload.
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Senior engineering · AI / ML
Senior AI / ML engineering for production systems.
Senior AI/ML engineering — generative AI, RAG, agents, MLOps, and the eval discipline production AI requires once it leaves the demo.
- In production
- 2018+
- Senior bench
- 30+ G6/G9 engineers
- Floor
- G6+ on client work
- Engagement
- Outcome-led, not hourly
Why senior, not contractor
AI / ML in production is a different problem than AI / ML on a laptop.
Most AI engagements ship a demo and call it production. Production AI needs eval harnesses, drift monitoring, prompt versioning, fallback behavior when the model is degraded, and observability that catches a regression before users do. Prosigns ships AI with the same operating discipline as any other production system — tests in CI, SLOs in dashboards, and runbooks for the day a vendor model changes its behavior overnight.
Senior floor
G6+ minimum
Bench depth
30+ G6/G9 engineers
In production
2018+
Engagement
Outcome-led SOW
Where AI / ML ships
6 sub-areas, grounded in shipped engagements.
Specific applications of AI / ML we’ve built and operate. Every example below maps to a real engagement, not a bullet on a stack-card.
01 02 Agentic systems
Multi-agent orchestration, tool use, structured outputs, evals against agent traces. ReAct, tree-of-thought, programmatic supervision.
03 MLOps + governance
MLflow, Weights & Biases, model registries, eval harnesses in CI, drift monitoring, prompt versioning, governance pipelines.
04 Computer vision
Document understanding, OCR, video analytics, medical imaging. PyTorch, ONNX, edge deployment via TFLite / Core ML.
05 Predictive analytics
Forecasting, churn, recommendations, anomaly detection. Calibrated probabilities, not point estimates. Interpretability where required.
06 Production deployment
FastAPI + Ray Serve / Triton / BentoML / vLLM. Batch + streaming inference, autoscaling, fallback behaviors, cost optimization.
Stack depth
The AI / ML ecosystem, owned.
Frameworks, libraries, and runtime tools the bench has shipped in production. Not a CV-skim — a working depth.
Foundation models
- OpenAI
- Anthropic
- Gemini
- Llama
- Mistral
- Cohere
RAG + retrieval
- LangChain
- LlamaIndex
- DSPy
- pgvector
- Weaviate
- Pinecone
Inference
- vLLM
- Triton
- BentoML
- Ray Serve
- TGI
- TensorRT
MLOps
- MLflow
- Weights & Biases
- Modal
- SageMaker
- Vertex AI
Evals + governance
- LangFuse
- LangSmith
- Phoenix
- Promptfoo
- DeepEval
Engagement models
Three ways to engage AI / ML engineers.
We don’t bill hourly contractors. Engagements run against outcomes — choose the shape that matches the work.
See engagement models- 01
Fixed-scope
Defined deliverable, fixed price
When the deliverable is clear and the scope is bounded — an MVP, a migration, a discrete platform build. Senior engineering against a written outcome, not against a body count.
- 02
Embedded squad
Senior product team, ongoing
When the work is product-shaped and the cadence is continuous. A senior pod (engineering + design + PM as needed) embedded into your team, with the practice lead co-piloting from HELM.
- 03
Managed services
Steady-state operations
When the system is running and needs ongoing engineering ownership — operations, SLO defense, release management, security and compliance evidence. Monthly retainer against a published SLA.
Selected work
AI / ML engineering that shipped.
Financial services
Production RAG platform for a regional bank's customer-service triage.
+47%first-contact resolutionHybrid retrieval over policy + product knowledge. Evaluation harness in CI gating prompt + retriever changes. Fallback to rules-based triage during model degradation. Survived the first regulatory examination.
Duration · 5 months
Brief us
Reply < 4 business hoursHave a AI / ML workload? Send a brief.
Five fields. Goes straight to the practice lead — not an SDR. We’ll reply with a senior engineer’s read on fit, scope, and the engagement model that suits the work.
FAQ
Questions buyers ask.
Everything below also appears in the proposal and the SOW — no surprises after signing.
Build vs. buy — when do we use Vertex / Bedrock / OpenAI vs. self-host?
Hosted (OpenAI, Anthropic, Bedrock, Vertex) when speed-to-market and capability ceiling matter and the data-residency / cost / latency profile fits. Self-host (Llama / Mistral on vLLM) when data residency, cost-at-scale, or fine-tuning specifics demand it. We model both paths against your workload.
How do you evaluate production AI?
Eval harnesses in CI before any prompt or model change ships. Per-use-case metrics — faithfulness for RAG, task success rate for agents, calibration for predictive models. Production-traffic dashboards (LangFuse / LangSmith / Phoenix) for drift detection and outlier inspection.
Is this staff-aug, or is it engineering-led delivery?
Engineering-led delivery. We don't bill hourly contractors against your JIRA board. Every engagement runs against a defined outcome with a senior engineer accountable from kickoff to operating cutover. If you genuinely need staff-aug — discrete bodies, your management, hourly rates — we'll be honest and route you to a partner that fits.
What seniority floor are we talking about?
G6 minimum (six-plus years in their craft) on every billable hour. Department leads are G9 or G10. We don't flex juniors onto the bench mid-sprint, we don't subcontract to delivery centers, and we don't dilute senior rates with mixed staffing. The bench in the proposal is the bench in production.
How does pricing work?
Three engagement models published at /engagement-models/. Fixed-scope for defined deliverables, embedded squads for ongoing product work, managed services for steady-state operations. Rates depend on seniority, engagement length, and region. Discovery + scoping conversation is free; SOWs are written against deliverables, not bodies.
Where are your engineers based?
Senior-only across Dallas, Doha, Lahore, and Islamabad. We staff against the engagement's needs (timezone, language, regulatory frame), not against arbitrary regional preferences. Most engagements run with a US/EU-aligned core and a follow-the-sun extended bench when the workload warrants it.
Will the engineers I see in the proposal be the engineers shipping the work?
Yes. We name the engineers in the SOW, attach their profiles, and they're on the kickoff. We don't bait-and-switch with senior reviewers and junior execution. If a named engineer needs to roll off the engagement (rare), we surface a replacement from the same seniority tier with explicit handoff.
Talk to a AI / ML lead
Senior AI / ML engineering, accountable from kickoff.
Bring the workload — we’ll bring a senior engineer plus the practice lead most relevant to the work. 30 minutes, no obligation, no junior reps.