Hire an AI/ML Engineer in India
AX3 places India-based AI/ML engineers — LLM application engineering, retrieval-augmented generation, agent workflows, model deployment and MLOps — on contract, contract-to-hire, permanent and dedicated pod engagements. Bench-ready engineers are typically presented within 48–72 hours; permanent search runs two to four weeks. Because AX3 delivers AI programmes itself, engineers come with evaluation, guardrail and deployment practice rather than notebook-only experience.
Talk to usWhat an AI/ML engineer actually does
An AI/ML engineer makes models usable in production. That covers retrieval pipelines and grounding, prompt and agent design, fine-tuning where it earns its cost, inference architecture and latency budgets, evaluation harnesses, and the guardrails and monitoring that keep an AI feature safe once real users reach it.
In enterprise settings most of the difficulty sits around the model, not inside it: access to trustworthy content, permissions that must be respected at retrieval time, evaluation that reflects the business task, and a rollback story when quality drifts. Engineers who have shipped and operated an AI feature — not just prototyped one — are the ones worth hiring.
LLM application engineering
RAG pipelines, grounding, chunking and permission-aware retrieval over enterprise content.
Agents and orchestration
Tool-using workflows, function calling, human-in-the-loop checkpoints and failure handling.
Evaluation and guardrails
Task-level evaluation sets, regression testing, safety filters, PII handling and output validation.
MLOps and deployment
Model serving, feature and vector stores, CI/CD for models, cost and latency monitoring, drift detection.
Four ways to engage — with pricing model and speed to start.
| Model | Best for | Pricing model | Speed to start |
|---|---|---|---|
| Contract | Proofs of value, AI feature builds and delivery surges | Monthly or hourly rate, time & materials | 48–72 hours for bench-ready engineers |
| Contract-to-Hire | First AI hires where the roadmap is still forming | Contract rate, then an agreed conversion fee | 1–2 weeks including calibrated shortlisting |
| Permanent | Building an in-house AI engineering capability | One-time placement fee with replacement guarantee | 2–4 weeks to offer |
| Remote Team | Ongoing AI product and platform capacity | Fixed monthly pod cost (AI engineer, data engineer, QA mix) | 2–3 weeks to a running pod |
Swipe the table sideways to see all columns.
Placed where AX3 already delivers.
Semiconductor
Engineering copilots over design and test data, anomaly detection and yield triage.
View skillsCPG & Retail
Retail execution assistants, demand signals and trade promotion decision support.
View skillsFinancial Services
Service copilots, document intelligence and controls-aware AI deployment.
View skillsHealthcare & Life Sciences
Document and evidence workflows inside validated, auditable environments.
View skillsHire an AI/ML Engineer in India — frequently asked questions
What is the difference between an AI/ML engineer and a data scientist?
A data scientist frames and answers questions with models and analysis. An AI/ML engineer builds the system that runs those models — retrieval, serving, evaluation, guardrails and monitoring. AI features that reach production usually need both skill sets.
Do AX3 AI engineers work with Salesforce Agentforce and Data 360?
Yes. The AX3 Salesforce and AI practices work together on Agentforce, Data 360 and Einstein deployments, so engineers can be placed into that stack as well as into open-source or hyperscaler AI platforms.
Can you help us move an AI prototype into production?
That is the most common request. Work typically starts with an evaluation harness and grounding review, then permissions, cost and latency, then deployment and monitoring — the parts prototypes skip.
How do you handle data privacy in AI projects?
Engineers work inside your environment and your data boundaries, apply permission-aware retrieval so users only see what they are entitled to, and put PII handling and output validation in place before a feature is exposed to users.
Talk to AX3 about hiring AI/ML engineers
Describe the AI use case and where it is stuck — we will match engineers who have taken that kind of feature to production.