Staff augmentation · India
Hire AI/ML Engineers in India.Vetted shortlists for your team.
Request pre-vetted AI/ML engineers in India. Shortlists in 2–3 weeks with clear pricing and engagement options for applied ML work.
Request profilesimport torchfrom transformers import AutoModelForCausalLM, TrainingArgumentsfrom peft import LoraConfig, get_peft_modelimport mlflowmodel = get_peft_model( AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype=torch.bfloat16), LoraConfig(r=16, lora_alpha=32, target_modules=["q_proj", "v_proj"]),)args = TrainingArguments( output_dir="checkpoints/invoice-llm", per_device_train_batch_size=4, bf16=True,)with mlflow.start_run(run_name="invoice-llm-v3"): trainer = Trainer(model=model, args=args, train_dataset=dataset) trainer.train(resume_from_checkpoint=True) mlflow.log_metrics({"eval_loss": trainer.state.best_metric})Core stack
- PyTorch & training
- DVC & MLOps pipelines
- MLflow & Weights & Biases
- Triton & FastAPI serving
- Hugging Face & LLMs
- RAG & retrieval
5+
Average years in applied ML
Engineers with production model delivery experience.
How we staff engagements
We support founders and engineering leaders who need capacity without a long permanent hire cycle. You get clear timelines, a defined vetting process, flexible engagement options, and pricing before interviews.
- Placement speed
- 2–3 weeks
- Vetting process
- 4-step screen
- Engagement models
- Flexible
- Pricing range
- Custom bands
Role intake, sourcing, and technical screening through to a shortlist you can interview. Typical start within 2–4 weeks after you select a profile.
Stack and experience match, communication check, live technical review with a senior engineer, and reference checks before profiles reach you.
Dedicated augmentation, contract staffing, or contract-to-hire. Engineers work in your repositories, tools, and time zones, with IP assigned to you.
Monthly or hourly bands by seniority and stack, quoted before interviews so finance and engineering can align on budget.
Engagement metrics
- Average years in applied ML
- 5+
- Inference cost reduction potential
- 60%+
- Fine-tune to staging deployment
- 2–4 wks
- Reproducible experiment tracking
- 100%
Engineers with production model delivery experience.
Typical savings from serving and batching improvements on past work.
Common timeline once data access and environment setup are ready.
Runs and artifacts tracked so results can be compared and audited.
Skills screened
Tools we staff
Profiles are screened against your training, serving, and MLOps tools.
PyTorch & training
Training jobs, evaluation, and model export.
DVC & MLOps pipelines
Data and model versioning for repeatable runs.
MLflow & Weights & Biases
Experiment tracking and promotion records.
Triton & FastAPI serving
Inference services with latency targets.
Hugging Face & LLMs
Fine-tuning and evaluation for LLM features.
RAG & retrieval
Retrieval pipelines and answer-quality checks.
Production monitoring
Drift, latency, and inference cost checks.
Feast & feature stores
Feature definitions for train and serve parity.
ONNX & model export
Portable export for your serving runtime.
Frequently asked questions
How long does it take to receive a shortlist?
Most roles are shortlisted in 2–3 weeks after intake. Timing depends on seniority, stack rarity, and how quickly feedback comes back on the first profiles.
How do you vet candidates before we interview?
We screen for stack and experience fit, run a communication check, complete a live technical review with a senior engineer, and verify references before profiles reach you.
What engagement models do you support?
Dedicated staff augmentation, contract staffing, and contract-to-hire. Engineers join your tools, repositories, and ceremonies. IP is assigned to your company.
What if the person is not a fit after starting?
We replace in up to 30 days at no extra sourcing fee. Tell us early so we can protect your timeline.
How does pricing work?
We quote monthly or hourly bands by seniority and stack before interviews. That keeps finance and engineering aligned on budget before you invest time in the process.
