AI Factory Builders · BFSI · 18 years

The AI strategy is done. Now someone has to ship Agentic AI.

Soho is the standing army of AI-native builders, ready to ship into your stack, on your repo, in days. Eighteen years placing the bench the world's top strategy firms, global consulting networks, and Tier-1 Financial Institutions rely on.

Get five profiles in 72 hours

72 hrsTime to first profile

38dMedian time to production

200+Collaborators · 3 continents

The market signal

AI Engineering Demand · 2024–26

+340%

Job postings citing agent, RAG, or LLMOps

22%

AI POCs that reach productionIndustry average. The other 78% never ship.

6.4 mo

Median time-to-hire for Senior AIFor roles HR systems can't yet code.

The reality

Demand has gone vertical. Supply hasn't moved.

Every enterprise has a deck on Agentic AI. Few have the engineers to make it real. The result is a four-dimensional shortage no traditional hiring channel was built for.

Reg. industries

Banks. Insurers. Asset Management.

AI ambition meets compliance reality. Audit trails, eval harnesses, model cards, data lineage. Most engineers have done one. Few have done all four.

Fortune 500

Every BU now needs AI.

Each business unit is racing to add AI to its core product. Centralized factories can't staff every roadmap. Internal hiring is six months and counting.

Growth-stage

Scale-ups can't pay FAANG.

Talent is being absorbed by hyperscalers and frontier labs at total comp the rest of the market can't match. Growth companies need senior bench, not zero-to-three.

Mid-market

Data, ambition, no bench.

Mid-market firms have data and use cases. Not the engineering muscle. They need productive senior bench they can rent and convert.

The stack we ship with

Production-grade across providers, frameworks, and infra.

Not opinionated. Buyer-flexible. We ship with what your codebase already trusts and adopt what your roadmap demands. Hyperscalers, frontier labs, open-source frameworks, and BFSI-native data systems. The bench is fluent across all of it.

OpenAI·Anthropic·Google Vertex·AWS Bedrock·Cohere·Mistral·Hugging Face·LangGraph·LangChain·AutoGen·CrewAI·DSPy·n8n·Pinecone·Weaviate·pgvector·ChromaDB·Qdrant·Snowflake·Databricks·BigQuery·Bloomberg·Blockgraph·BrainTrust·LangSmith·Arize·Datadog·Helicone·Kubernetes·Terraform·Helm·Vercel·Modal·Replicate·

01LLM Providers+

OpenAI Anthropic Google Vertex AWS Bedrock Cohere Mistral Hugging Face

02Orchestration & Agents+

LangGraph LangChain AutoGen CrewAI DSPy n8n

03Vector & Retrieval+

Pinecone Weaviate pgvector ChromaDB Qdrant

04Data & Warehouse+

Snowflake Databricks BigQuery Bloomberg Blockgraph

05Evals & Observability+

BrainTrust LangSmith Arize Datadog Helicone

06Infra & Deploy+

Kubernetes Terraform Helm Vercel Modal Replicate

Stack-agnostic by designBring your own stack. We adapt. Whether your AI factory runs on Bedrock or Vertex, your retrieval lives on Pinecone or pgvector, your evals run on BrainTrust or in-house, the bench has shipped on it.

The wave

Agentic AI is the priority.

Each generation of AI required a different engineering discipline. Most enterprises are still hiring for wave 2. Soho started recruiting for wave 3 in 2024. The bench is ready for wave 4.

2022 · Wave 1

Prompt apps.

ChatGPT wrappers. Single-shot completions. Demos in a weekend. Nothing stuck in production.

PromptsSingle LLM

2023–24 · Wave 2

RAG systems.

Embeddings, vector stores, retrieval-augmented generation. Where most enterprises are still hiring. Plateauing.

RAGPineconepgvector

2025–26 · Wave 3

Agentic AI.

Multi-agent systems, tool use, autonomous workflows, policy routing. Non-deterministic, harder to ship, requires new disciplines.

LangGraphAutoGenEvals

2027+ · Wave 4

Productized AI.

Governed, observed, evaluated, audited, adopted. AI as a load-bearing product feature. Most haven't started.

GovernanceAdoptionFinOps

The practice · Lead

The AI Factory Builder Practice.

Eight role specializations, calibrated for the agentic wave. Forward Deployment Engineer is the new lead role, the hybrid builder who deploys AI directly into your environment.

100% hands-on engineering · individual contributors · no people management.Backed by a Soho engagement lead at no additional cost, for SOW oversight, never people management.

SR · 8+ YRS · CLIENT-EMBEDDED

Forward Deployment Engineer

Ships AI on your code, in your repo, owning the path from SOW to adoption.

The hybrid builder who combines deep technical chops with stakeholder fluency. Independent execution. Bias toward action. Comfort in ambiguity.

PythonLangGraphCloud-native

96 hr RFP turnaround

SR · 8–10+ YRS

AI-Native Software Engineer

Owns the production code path end-to-end across providers and patterns.

The generalist who ships. Comfortable across OpenAI, Anthropic, Bedrock, Vertex. Iterates in real-world environments.

PythonOpenAIAnthropicBedrock

72 hr RFP turnaround

LEAD · 10+ YRS

Agent & Orchestration Architect

Designs the agent topology. Without this role, multi-agent systems fail at scale.

Policy-based routing, tool invocation, retrieval orchestration. The architect who keeps your agentic systems coherent under load.

LangGraphAutoGenDSPy

72 hr RFP turnaround

MID-SR · 6–8 YRS

RAG & Retrieval Specialist

Makes generation grounded. The plumbing every production AI needs.

Indexes, embeds, evaluates retrieval at scale. Hybrid search, re-ranking, citation accuracy, sub-second latency.

PineconeWeaviatepgvector

72 hr RFP turnaround

SR · 7+ YRS

AI Eval Engineer

Keeps your agents from regressing in production. Required for compliance-grade AI.

Builds eval harnesses for accuracy, latency, cost, and safety. Golden datasets, regression suites, drift detection.

BrainTrustDSPypytest

96 hr RFP turnaround

SR · 8+ YRS

MLOps for LLMs

CI/CD for non-deterministic systems. The reason your agents don't break Sunday at 3am.

Drift detection, rollback strategies, reproducible deploys. Production reliability discipline.

TerraformKubernetesDatadog

72 hr RFP turnaround

SR · 8–10 YRS

AI Platform Engineer

Builds the platform every other engineer ships on. Multiplies team velocity.

Token budgeting, model routing, observability, cost attribution. Powers the AI factory itself.

GoPythonPostgres

72 hr RFP turnaround

MID-SR · 6–9 YRS

AI Product Engineer

Bridges LLM glue to product UX. Without this, end-users don't adopt.

Streaming UIs, latency-tolerant interaction patterns, cost-aware design. Adoption metrics owned end-to-end.

ReactNext.jsVercel AI

72 hr RFP turnaround

The depth · Six core practices

Eighteen years of BFSI muscle, behind every AI engagement.

AI Factory is the wedge. BFSI depth is the moat. Senior bench across six core practices, sharpened by eighteen years of work with Tier-1 Financial Institutions, the Big Four, and the world's top strategy firms. Available alongside every AI engagement, billed direct.

Risk Management

Quantitative risk analytics, third-party risk, risk IT, governance and compliance.

Banks · Insurers · Asset Mgmt

Governance

Enterprise frameworks, controls testing, audit-grade documentation. Aligning ESG and regulatory.

Boards · Audit · ESG

Compliance

MRA and MRIA resolution. Working closely with the Fed, SEC, FINRA, OCC, DFS.

Fed · SEC · FINRA · OCC · DFS

Regulatory Reporting

Capital markets and financial regulation. Reporting transformation across investment, commercial, asset management.

Capital Markets · IB · Asset Mgmt

Technology

Cloud migration, data and audit, digital system replacement, lean software management. AWS · Azure.

AWS · Azure · Cloud-native

Operations

Program and project management, target operating models, business process improvement, post-merger.

PMO · TOM · M&A integration

Pilot, SOW, or Direct Bench. Three contracting modes across all six practices. MSP and VMS friendly. Pre-cleared at most large enterprise procurement programs.

See engagement modes

What the bench has delivered

Built. Shipped. Iterated. Adopted.

The bench's results in 2025, including agentic AI in production. Engagements with the world's top strategy houses, global consulting networks, and Tier-1 Financial Institutions.

47production systems shipped

94%12-month adoption

38dmedian time-to-production

$11Mmodel cost saved

NUMBERS VALIDATED BY CLIENT OBSERVABILITY AND FINANCE SYSTEMS · INDEPENDENT AUDIT AVAILABLE UNDER NDA

Top strategy firmLive

Multi-agent eval harness for AI Factory.

Built the eval and observability layer for a centralized AI Factory at a top-three strategy house. Six engineers, fourteen weeks.

38dTo first ship

94%Adoption

$2.1MCost saved

Tier-1 InsurerAgentic · Live

Agentic claims triage at production scale.

Multi-agent claims first-pass review. Policy-based routing to human adjusters by complexity, fraud signal, jurisdiction. Compliance-pre-cleared.

71%Auto-resolution

4.2xThroughput

0Compliance flags

Global consulting networkLive

GenAI Factory MLOps backbone.

CI/CD for prompts and models, drift detection, rollback. Replaced ad-hoc notebooks with reproducible Helm release workflow.

62%Faster deploys

9→1MTTR (hr)

12Teams onboarded

Three engagement modes

Embedded. SOW. Direct Bench.

Soho aligns to your delivery model, not the other way around. MSP-friendly, VMS-compatible, MSA-ready. Pick the engagement that matches how your procurement actually buys.

Embedded.

Single senior engineer · client-site · open to convert.

Best forDirect team integration, senior individual contributors, clients who want IP ownership from day one.

LengthOpen-ended
Bench size1 senior engineer
PricingT&M rate card
MSA neededYes (we have most)
Convert to permAvailable

SOW.

Deliverable-based · multi-engineer · fixed-scope.

Best forKnown scope, predictable budgets, audit-friendly procurement.

Length3–12 months
Bench size2–8 engineers
PricingFixed-bid by deliverable
MSA neededYes (we have most)
Engagement leadIncluded

Direct Bench.

T&M contingent · MSP/VMS friendly · scales with demand.

Best forOngoing programs, flexible scaling, existing supplier programs.

LengthOpen-ended
Bench sizeScales 1–N
PricingT&M rate card
MSP friendlyBeeline · Fieldglass · Pontoon
VMS integrationsActive

From RFP to day-one productive

Fourteen days. Five milestones. A lot happens in between.

What looks like a clean line is anything but. Here's the visible journey, the hidden work, and the effort behind each phase, so your engineer ships on day fourteen.

72 hrsFive vetted profiles delivered.

Day 0Day 1–3Day 4–7Day 8–10Day 12–14

Day 0

RFP received.

You seeConfirmation. Scope acknowledged. Engagement lead named.

Behind the scenes

Brief decoded across 8 role taxonomies
Bench portfolio scan kicks off
Compliance pre-clear flag set
SOW skeleton drafted in parallel

Day 1–3

Five profiles delivered.

You seeFive vetted profiles with shipped portfolios. Quality over quantity.

Behind the scenes

200+ engineers screened from active bench
Stack alignment matched to your RFP
Two rounds of internal calibration
Code samples and shipped systems verified
References pre-warmed, availability confirmed

Day 4–7

Technical interviews.

You seeLive system design, code walkthrough, eval discussion with your tech leads.

Behind the scenes

Each candidate prepped on your stack
Mock interview run internally
Post-interview debrief sent within 24h
Profile recommendation letter prepared

Day 8–10

Reference checks.

You seeCompliance pre-clear, background, references. SOW finalized.

Behind the scenes

3+ references contacted per candidate
Background and identity verification
SOW redlines aligned with your procurement
MSA cross-check (we have most)
Day-1 onboarding pack drafted

Day 12–14

Day-one productive.

You seeEngineer onboards. In your repo. Shipping.

Behind the scenes

Repo access and credential setup
Stack briefing tailored to your codebase
Engagement lead intro, weekly cadence locked
First sprint scope co-signed

Why we win

Compared to the alternatives.

Most enterprises evaluating AI talent partners look at three kinds of provider. Here's where each falls short, and why Soho wins.

SohoAI-native, on-shore senior

On your repo, in days.

~1.4x markup. 8+ yrs avg. Five profiles in 72 hours. Code in your repo. Engagement lead included.

Generalist platforms

Junior-heavy, no AI specialization.

Volume model. You sift fifty profiles to find one fit.

Strategy-led firms

Heavy markup, slide-first.

3–5x markup. Strategy-led, execution-second. Often subcontracts the actual building.

Tech Partners

Cloudera

Google Vertex

Palantir

Databricks

Snowflake

Careers

A place to thrive.

Soho's greatest asset is its people. We attract top-tier AI-native talent and cultivate a team rich in diversity, of thought, background, and experience, all united by a commitment to delivering Growth-for-Good.

Apply now

Trusted by the firms that own digital transformation

Our
Impact

To new beginnings…

We have always been excited about challenges and the process to overcome them. As a team, it has always been possible to overcome all obstacles with Trust, Integrity and Innovation. The past couple of years have been a bumpy ride as the world was facing a common enemy. When we look back, we come to realize the enormous change in operations, not just to Soho, but to the entire industry. As we approach the end of another calendar, it's time to set newer, bigger and ambitious goals and focus on making a development as we have done year after year….

AI Engineering Demand · 2024–26

Demand has gone vertical. Supply hasn't moved.

Banks. Insurers. Asset Management.

Every BU now needs AI.

Scale-ups can't pay FAANG.

Data, ambition, no bench.

Production-grade across providers, frameworks, and infra.

Agentic AI is the priority.

Prompt apps.

RAG systems.

Agentic AI.

Productized AI.

The AI Factory Builder Practice.

Forward Deployment Engineer

AI-Native Software Engineer

Agent & Orchestration Architect

RAG & Retrieval Specialist

AI Eval Engineer

MLOps for LLMs

AI Platform Engineer

AI Product Engineer

Eighteen years of BFSI muscle, behind every AI engagement.

Risk Management

Governance

Compliance

Regulatory Reporting

Technology

Operations

Built. Shipped. Iterated. Adopted.

Multi-agent eval harness for AI Factory.

Agentic claims triage at production scale.

GenAI Factory MLOps backbone.

Embedded. SOW. Direct Bench.

Embedded.

SOW.

Direct Bench.

Fourteen days. Five milestones. A lot happens in between.

RFP received.

Five profiles delivered.

Technical interviews.

Reference checks.

Day-one productive.

Compared to the alternatives.

On your repo, in days.

Junior-heavy, no AI specialization.

Heavy markup, slide-first.

A place to thrive.

Our Impact

To new beginnings…

Our
Impact