Hire AI Developers

Expert at connecting GPT-4o, Gemini, Claude, and Mistral APIs into production applications, including streaming, function calling, tool use, and multi-modal inputs.

Builds production-grade Retrieval-Augmented Generation pipelines combining vector search with LLM generation, enabling AI assistants that answer from private knowledge bases.

Adapts open-source LLMs to domain-specific tasks using PEFT techniques, reducing compute cost by 70%+ while achieving performance comparable to full fine-tuning.

Expert in building agentic AI systems that plan, use tools, and execute multi-step tasks autonomously, integrating with APIs, databases, and third-party services.

Deep expertise in structured prompting strategies, chain-of-thought, few-shot, and ReAct patterns that improve output quality and make LLM behavior predictable at scale.

Implements output validation, hallucination detection, content moderation, and PII redaction to ensure AI systems behave safely within defined policy boundaries in production.

Primary AI development language used for model training, API development, data pipelines, and scripting automation workflows across all AI project types.

Advanced PyTorch for custom architectures, training loops, CUDA optimization, distributed training, and exporting to ONNX and TorchScript for production inference.

Building, training, and deploying models using TF Serving for production inference, TFLite for edge deployment, and Keras for rapid prototyping and model iteration.

Expert-level use for building production RAG pipelines, agentic workflows, document indexing, and memory-enhanced conversation systems integrated into web and mobile apps.

Builds high-performance AI inference APIs using FastAPI, async handling, request validation, model caching, and OpenAPI documentation ready for production load from day one.

Hands-on expertise with Scikit-Learn pipelines, preprocessing, and model selection, plus XGBoost, LightGBM, and CatBoost for high-performance tabular ML at enterprise scale.

Designs robust ETL/ELT pipelines that feed AI models with clean, validated, and consistently formatted training data, built to handle millions of records at production scale.

Expert at handling messy real-world data, deduplication, outlier removal, class imbalance correction, annotation management, and synthetic data generation for model training.

Proficient in building and querying vector databases for semantic search, recommendation systems, and RAG pipelines with hands-on experience across all major providers.

Strong command of relational and document databases used to store, version, and serve training data, model outputs, embeddings, and user interaction logs for AI systems.

Skilled at uncovering patterns in complex datasets and communicating insights clearly, using interactive dashboards and model interpretability tools stakeholders can act on.

Processes large-scale datasets using Spark for distributed ML, enabling feature engineering and training on datasets that exceed single-machine memory limits.

End-to-end experience with AWS AI/ML stack, model training and deployment on SageMaker, generative AI via Bedrock, and serverless inference with Lambda and Step Functions.

Experienced with Google's end-to-end AI platform, Vertex AI model training, deployment pipelines, BigQuery ML for in-database training, and Gemini API integration at scale.

Proficient in Azure OpenAI Service, Cognitive Services, and Azure ML, deploying enterprise-grade AI models with security compliance, role-based access, and SLA-backed uptime.

Packages AI inference services into Docker containers and orchestrates them with Kubernetes, enabling auto-scaling, zero-downtime deployments, and consistent dev-to-prod environments.

Sets up automated ML pipelines with continuous training, evaluation gating, model registry versioning, and A/B deployment, keeping models current without manual intervention.

Implements real-time model observability, monitoring for data drift, concept drift, prediction latency anomalies, and automated retraining triggers to keep models accurate in production.

Expert in object detection, image classification, and segmentation, deploying YOLO, Vision Transformers, and SAM models for real-time inference in security, retail, and healthcare use cases.

Deep NLP expertise from tokenization and embeddings to full document understanding, using transformer models fine-tuned on domain-specific corpora for production-grade NLP pipelines.

Builds real-time and batch audio AI systems, automatic speech recognition, speaker diarisation, TTS synthesis, and voice cloning for AI assistant and contact center applications.

Deploys and fine-tunes image generation models for e-commerce, media, and creative applications, including custom brand-trained models using DreamBooth and ControlNet pipelines.

Builds AI systems that reason across text, images, audio, and video simultaneously, using GPT-4o Vision, Gemini 1.5 Pro, and LLaVA for complex cross-modal enterprise tasks.

Builds dense vector search and recommendation engines, enabling intelligent document retrieval, product discovery, and knowledge graph queries at scale with hybrid search strategies.