Hire AI Engineers
With Softeko
20+
AI Engineers
25+
AI Projects Delivered
95%
Client Repeat Rate
90+
Enterprise Deployments
Vetted AI 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 AI Engineers in a matter of days. This is just a small sample of the high-caliber talent working for us already.
✓
Designed NLP-driven chatbot serving 1M+ users with PyTorch and Azure AI.
Specialized in deploying high-accuracy models with optimized inference.
Khulna, Bangladesh 4–6h overlap (CET)
✓
Built a RAG platform using LangChain and a vector store; improved support deflection by 85% with domain tools and guardrails.
Dhaka, Bangladesh • 4–6h overlap (EST)
✓
Architected vision inference over gRPC with ONNX runtime at the edge; achieved 50% faster throughput on commodity GPUs.
Dhaka, Bangladesh • 4-6h Overlap (CET)
✓
Deployed LLM services on Sagemaker with autoscaling and OTel traces; built safe APIs with rate limits and abuse detection.
Rajshahi, Bangladesh • 4-6h Overlap (ET)
✓
Delivered 15+ GenAI apps with tool use and retrieval; built safe prompts, eval harnesses, and frontends integrated with React.
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 AI Experts,
Ready When You Are
Skip weeks of screening. Get instant access to pre-vetted AI 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 AI Engineers Offer
From startups to enterprises, our AI engineers deliver intelligent systems that perform.
End-to-End AI Applications
chatbots, copilots, automation platforms.
Natural Language Processing (NLP)
text classification, LLM integration, conversational AI.
Computer Vision
image recognition, video analytics, AR/VR AI.
Generative AI
fine-tuning LLMs, vector databases, LangChain.
AI System Architecture
multi-component orchestration & APIs.
MLOps for AI Systems
CI/CD pipelines, monitoring, retraining.
Cloud AI Integration
AWS Sagemaker, Azure AI, Google Vertex.
Responsible AI
bias detection, explainability, compliance.
Domain-Specific AI
healthcare, fintech, logistics, e-commerce.
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 AI Engineers With Softeko?
AI System Design
End-to-end intelligent apps.
NLP & LLMs
Advanced text + conversation.
Computer Vision
Scalable image/video AI.
GenAI & Vector DBs
Next-gen retrieval-augmented apps.
MLOps & Cloud
Production-ready deployments.
Responsible AI
Ethical, compliant systems.
Flexible Engagement Models
Scale your AI team up or down to exactly the size you need:
- Dedicated Pods : 1–3 engineers 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 AI 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 AI pro for one sprint and see immediate impact.
How Long Does It Take to Hire AI 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 AI Talent Pool | 48-72 hours | Pre-vetted AI experts ready to start immediately |
Launch Your Project in 2 Business Days
No job-board delays. Zero sourcing overhead. Hire AI Engineers instantly and hit the ground running.
Interview Questions to Ask Before You Hire AI Engineers
Identify the right fit faster with these targeted technical and behavioral questions.
AI System Architecture
What does an AI Engineer do?
They design end-to-end intelligent systems integrating ML, NLP, CV, and decision logic.
How do you integrate AI into apps?
Expose models as REST or gRPC services and orchestrate workflows.
What makes AI production-ready?
SLAs, monitoring, CI/CD pipelines, and rollback strategies.
How do you design multi-component AI?
Partition pipelines: data ingest → feature store → model serve → API.
Natural Language Processing (NLP)
How do you fine-tune an LLM?
Use domain data with transformers adapters or LoRA layers.
Best way to manage embeddings?
Store in vector DBs like Pinecone or Weaviate.
How do you reduce hallucinations?
Implement RAG (retrieve + ground facts before prompting).
How to scale a chatbot?
Shard by tenant and cache responses for repeat queries.
Computer Vision
How to deploy vision models at scale?
Use TensorRT or ONNX Runtime with GPU batching.
How to handle real-time video AI?
Stream via GStreamer and optimize with hardware acceleration.
What is multimodal AI?
Combining text, image, or audio embeddings into one pipeline.
How to monitor CV systems?
Track FPS, accuracy drift, and false-positive rates.
Generative AI (GenAI)
What is Retrieval-Augmented Generation (RAG)?
Retrieval fetches facts; LLM generates grounded, relevant output.
When fine-tune vs prompt?
Prompt for quick tasks, fine-tune for domain accuracy.
How to integrate LangChain?
Chain prompts, memory, and vector DB queries for LLM apps.
How to prevent prompt-injection?
Input sanitization + whitelisting tools + guardrails.
MLOps & Deployment
What is MLOps in AI?
Automation for model training, serving, monitoring, and retraining.
Preferred serving stack?
TorchServe, Triton, or FastAPI containers.
Blue/green vs canary for AI?
Blue/green for safe swaps; canary for gradual rollout.
How to detect drift?
Compare live vs training data with PSI/KS tests.
Red Flags to Watch For
⭕ No system-level design skills.
⭕ Only ML modeling, no integration.
⭕ Ignores bias/explainability.
⭕ Deployment experience.
Additional Interview Questions
Responsible AI
Unit vs. Instrumentation tests, when to use each?
To ensure fairness, compliance, and ethical AI use.
How do you explain AI output?
Use SHAP, LIME, or attention heatmaps.
How to secure AI pipelines?
Encrypt data, manage secrets with KMS, apply RBAC.
How to comply with GDPR?
Mask PII, track consent, log model decisions.
Data Engineering for AI
What’s a feature store?
Central repository for re-usable features (Feast).
How do you ensure data quality?
Contracts, schema validation, expectation suites.
Streaming vs batch pipelines?
Streaming for real-time; batch for heavy ETL.
How do you handle big data?
For complex multi-stream compositions using rich operators, classic reactive pipelines.
Cloud AI & Platforms
Which cloud AI platforms do you use?
AWS SageMaker, Azure AI, GCP Vertex.
When to use Kubernetes?
For GPU scheduling and scaling inference workloads.
How to optimize AI cost?
Batching, spot instances, cache embeddings/responses.
How to manage secrets/configs?
Key Vault, AWS Secrets Manager, or GCP Secret Manager.
Performance & Reliability
How to speed up inference?
Quantization, pruning, and TensorRT optimizations.
How to cache AI results?
Store embeddings + responses in Redis or vector DB.
How to design for scale?
Autoscale pods with HPA and circuit breakers.
What SLOs matter most?
Latency (p95), accuracy, and cost/request.
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 does an AI Engineer do?
They build and integrate full intelligent systems (NLP, CV, GenAI).
2. How fast can Softeko provide AI Engineers?
Within 48-72 hours from our vetted talent pool
3. Can AI Engineers also train models?
Yes, but their strength is building complete AI-powered apps.
4. Do Softeko engineers handle LLM projects?
Yes—chatbots, copilots, and RAG-based enterprise assistants.
5. Are they experienced with cloud AI platforms?
Yes, AWS SageMaker, Azure AI, and Google Vertex.
6. Can I scale my AI team up or down?
Absolutely—start with 1 engineer, expand into a pod as needed.
With Softeko