Hire ML Engineers
With Softeko
ready to start in 72 hours.
40+
ML Engineers
25+
Projects Delivered
95%
Client Repeat Rate
60+
Production Models
Vetted ML Talent
Get the right talent fast, start building in just 2-3 days.
Fast Onboarding
Only the best pass rigorous vetting process.
Innovative Projects
Hire one expert or a full team, scale as needed.
Proven Results
With the project - every step to ensure success.
Skip the Hassle of Recruitment
Onboard our senior ML engineers in a matter of days. This is just a small sample of the high-caliber talent working for us already.
✓Quantized CNNs to INT8, exported ONNX, and served on Triton with dynamic batching and CUDA graphs for real-time video.
Khulna, Bangladesh 4–6h overlap (CET)
✓Built pipelines on Kubeflow/Vertex; model registry + canary; drift alerts with data/label checks and rollback playbooks.
Dhaka, Bangladesh • 4–6h overlap (EST)
✓Fine-tuned transformer encoders, added retrieval scoring for RAG, and built eval suites with grounding checks to cut hallucinations.
Dhaka, Bangladesh • 4-6h Overlap (CET)
✓
Trained ranking models in PyTorch, tracked experiments in MLflow, and shipped
low-latency inference with feature caching and vector search.
Rajshahi, Bangladesh • 4-6h Overlap (ET)
✓Built two-tower retrieval + re-rankers; centralized features in a store; added A/B guardrails to ship safe, measurable uplifts.
Dhaka, Bangladesh • 4–6h overlap (EST)
✓
Implemented 3-D Secure payments and offline caching for a delivery app; targeted FCM campaigns increased
reorders by 24%. Deep experience with Retrofit/OkHttp interceptors, resilient
Room sync, and Firebase Analytics for growth experiments.
São Paulo, Brazil • 2–4h overlap (ET)
Top ML Expert,
Ready When You Are
Skip weeks of screening. Get instant access to pre-vetted ML experts who can:
- Build scalable, high-performance systems
- Contribute from day one, no hand-holding required
- Align with your stack, tools, and workflows
- Collaborate seamlessly with existing teams
- Hit sprint goals without onboarding delays
Services Our ML Engineers Offer
From startups to enterprises, our ML Engineers deliver apps that perform on every device and every release.
Data & Feature Engineering
Batch/stream ingestion, joins, quality, and feature stores.
Model Development
Classification, regression, ranking, and forecasting.
NLP & Retrieval
Transformers, embeddings, RAG, and eval harnesses.
Computer Vision
Detection, OCR, tracking, and real-time pipelines.
Recommender Systems
Retrieval, re-ranking, bandits, and feedback loops.
MLOps & CI/CD
Pipelines, registries, canaries, and rollbacks.
Serving & Scaling
Triton/SageMaker/Vertex, autoscaling, GPU/CPU mix.
Monitoring & Drift
Data/label shift, fairness, alerts, and SLOs.
Responsible AI & Privacy
PII handling, guardrails, red-teaming, and audits.
Our Operational Blueprint: How Softeko Works
Our proven methodology ensures successful project delivery from concept to deployment.
- Step 1
Discover Needs
We start by understanding your workflows, pain points, and goals.
→ Analysis - Step 2
Build Strategy
We design a roadmap customized to your tech, team, and timelines.
→ Planning - Step 3
Assign Experts
Your project is powered by a dedicated, domain-aligned team.
→ Matching - Step 4
Deliver in Sprints
We execute in agile sprints with full transparency and feedback.
→ Execution - Step 5
Optimize Continuously
Post-launch, we refine and adapt to ensure lasting results.
→ Enhancement
Why Hire ML Engineers With Softeko?
Modeling Depth
Strong baselines, real lifts.
MLOps Maturity
Pipelines, registry, canary.
Data Reliability
Tests, contracts, lineage.
Cloud & GPU Savvy
Cost-aware scaling.
Evaluation Culture
Offline + online guardrails.
Security & Privacy
PII controls, reviews.
Flexible Engagement Models
Scale your team up or down to exactly the size you need:
- Dedicated Pods : 1–3 developers fully focused on your roadmap
- Staff Augmentation : integrate seamlessly with your in-house squad
- Short-term Sprints : bring on experts for rapid feature bursts
- Long-term Partnerships : retain knowledge, avoid ramp-up delays
100% Vetted Talent
Only the top 1% of ML engineers pass our rigorous screening.
72-Hour Onboarding
Your first expert codes within three days, no delays.
Effortless teamwork
Engineers adapt instantly to your tools, processes, and culture.
Guaranteed Results
We tie delivery milestones directly to your KPIs.
7-Day Pilot Engagement
Risk-free trial, onboard an ML pro for one sprint and see immediate impact.
How Long Does It Take to Hire ML Engineers?
| Platform | Avg. Time to Hire | What’s Involved |
|---|---|---|
| Traditional Job Boards | 10–14 days | Job posts, resume screening, multi-round interviews, onboarding paperwork |
| In-House Recruiting | 3–6 weeks | HR screening, technical tests, salary negotiation, notice periods |
| Softeko ML Talent Pool | 24–48 hours | Pre-vetted ML experts ready to start immediately |
Launch Your Project in 2 Business Days
No job-board delays. Zero sourcing overhead. Hire ML engineers instantly and hit the ground running.
Interview Questions to Ask Before You Hire ML Engineers
Identify the right fit faster with these targeted technical and behavioral questions.
Problem Framing & Baselines
How do I pick the right success metric for my ML problem?
Tie to business outcome choose offline metric that predicts it (e.g., AUC for ranking, MAE for pricing) and define "win" thresholds.
What's the fastest way to build a strong baselines?
Start with sklearn + XGBoost and robust CV; log runs in mlflow; set a simple “champion” to beat.
How do I prevent target leakage during training?
Time-based splits, drop post-event features, and validate with feature importance/anomaly checks on future windows.
How should I split data to avoid leakage?
For time series, use rolling origin TimeSeriesSplit; for users, group by entity with GroupKFold.
Data & Feature Engineering
How do I make features reproducible across batch and online?
Define in a feature store (e.g., Feast) with one registry; use the same transformations for online and offline.
Best practice for missing values and outliers?
Impute by logic (median/forward-fill) and winsorize or clip; document choices in data contracts and tests.
How do I reduce train/serving skew?
Log served features, compare to training stats nightly, and alert on drift; pin library versions and serialization formats.
What checks catch covariate shift early?
PSI/KS tests per feature, label distribution deltas, and model score calibration across time slices.
Modeling & Training
What’s a good hyperparameter tuning setup?
Use Optuna or Ray Tune with MedianPruner/ASHA; cap trials; log params/metrics to mlflow.
How should I validate time-dependent models?
Use walk-forward CV; keep lags/horizons consistent with production cadence.
How do I handle class imbalance cleanly?
Tune thresholds, use cost-sensitive losses or class_weight, and evaluate PR-AUC, not just ROC-AUC.
How do I make training deterministic?
Seed numpy, framework, and dataloader; set torch.backends.cudnn.deterministic=True; fix workers.
NLP & Retrieval (RAG)
How do I index documents for fast retrieval?
Embed with sentence-transformers; store in FAISS/HNSW (IndexHNSWFlat); tune M/efSearch.
How do I evaluate a RAG system reliably?
Use groundedness/faithfulness tasks, answer similarity, and retrieval recall@k; keep a frozen eval set.
How can I cut token costs without losing quality?
Cache prompts, compress context (dedupe, rerank with CrossEncoder), and constrain output via schemas.
How do I reduce hallucinations?
Cite sources in prompts, add guardrails, and prefer extractive generation over freeform when possible.
Computer Vision
What’s an effective augmentation policy?
Use RandAugment/AutoAugment; ensure class-balanced sampling; validate with ablation runs.
Mixed precision, when and why?
Enable torch.autocast/AMP for CNNs/ViTs to boost throughput; watch for numerics on older GPUs.
How do I export to ONNX/TensorRT correctly?
Freeze to ONNX (opset pinned), run polygraphy checks, then compile INT8 with proper calibration.
How do I batch video inference in real time?
Use Triton dynamic batching, sequence batching for streams, and CUDA graphs to cut launch overhead.
Red Flags to Watch For
⭕ Missing baselines or ablations.
⭕ No drift monitoring/alerts.
⭕ No reproducibility or seeds.
⭕ Only offline metrics; no SLAs.
Additional Interview Questions
Recommender Systems
What’s a robust two-tower retrieval setup?
Train user/item towers with in-batch negatives; ANN (FAISS/ScaNN) for candidates; re-rank with a shallow MLP/GBDT.
How do I solve cold-start?
Fall back to content-based or popularity priors; bootstrap with metadata and small exploration budgets.
How do I avoid feedback loops and bias?
Log propensities, use IPS/DR estimators, and cap exposure; rotate exploration traffic.
How should I A/B test recsys safely?
Pre-define guardrails (CTR, D1 retention, complaint rate), SRM check, and ramp up via canaries.
MLOps Pipelines
What belongs in an MLflow Model Registry?
Model artifacts, signature/schema, environment (conda.yaml), metrics, and stage (Staging/Production).
How do I orchestrate reliable training pipelines?
Airflow/Kubeflow with idempotent tasks, retries + jitter, and data-aware scheduling; store outputs immutably.
How do I backfill safely?
Run fixed-date jobs with frozen inputs; write to new partitions; verify and swap pointers.
How do I ensure reproducibility end-to-end?
Containerize (Dockerfile), pin deps, log git SHA + data snapshot, and record seeds.
Serving & Performance
Which serving stack fits low-latency models?
NVIDIA Triton (multi-framework, dynamic batching), TF Serving, or TorchServe; choose GPU/CPU by model profile.
How do I keep p95 latency down?
Enable dynamic batching, use quantized FP8/INT8 where safe, pre-load weights, and warm the autoscaler.
What’s a good canary/rollback plan for models?
Header/percentage routing, shadow traffic, and quick rollback to previous registry version on regressions.
How do I keep online/offline features consistent?
One feature repo, same transformations, and write-ahead logs; alert on skew between stores.
Monitoring, Drift & Safety
Which SLIs should I track in production?
p95/p99 latency, error rate, feature drift, label delay, and business KPIs; tie to SLOs.
How do I detect drift without labels?
PSI/KS on features, embedding drift distance, and canary metrics vs control.
What’s the right way to handle PII/secrets?
Encrypt at rest/in transit; redact logs; store keys in KMS/Vault; never in code.
What does a good incident runbook include?
Triage steps, rollback command, contact map, timelines, and after-action follow-ups.
Checkout Other Experts
With our IT staff augmentation services, you skip the headaches of hiring and managing admin tasks. We handle all the legwork, so you get top-notch specialists with real-world experience, ready to dive into your project with no hassle and no wasted time.
Testimonial
Since 2013, Softeko has helped businesses scale efficiently with top-tier IT professionals. Our customized IT staff augmentation services bridge talent gaps and boost your team’s productivity with speed and flexibility.
Questions? We've Got Answers.
1. What stacks do your ML engineers actually use?
Python, PyTorch/TF, scikit-learn/XGBoost, MLflow, Airflow/Kubeflow, Feast, Triton/TF-Serving, SageMaker/Vertex, OpenTelemetry, and FAISS.
2. Can I hire for short-term delivery?
Yes. Whether you need to build fast or scale support, we offer flexible engagement models.
3. How fast can I onboard someone?
We can match you with vetted expert and initiate onboarding within 48-72 hours.
4. Will I get to interview the developers?
Absolutely. You’ll have the option to interview and assess shortlisted developers before making a final decision.
5. Are the developers available in my time zone?
Yes. We provide global talent with overlapping work hours and full-time availability in your preferred time zone.
6. Can I scale the team up or down?
Yes. Scale up during critical phases or reduce size post-release. No long-term lock-ins.
With Softeko