Computer Vision

– Conduct in-depth audits of existing data infrastructure, pipelines, and governance frameworks

– Evaluate AI-readiness and identify high-value use cases through feasibility assessments and risk analysis

– Design bespoke AI strategies aligned with business objectives, regulatory constraints, and market dynamics

– Build phased roadmaps for end-to-end AI transformation—from proof of concept to enterprise-scale deployment

– Architect goal-driven agents using recurrent memory, planning modules, and dynamic toolchains

– Integrate retrieval-augmented generation (RAG), vector databases, and environment feedback loops

– Deploy agents for knowledge management, workflow automation, research co-pilots, and ops intelligence

– Implement safety layers, feedback alignment, and fine-grained observability for agent behavior control

– Design and train computer vision models for detection, classification, segmentation, and OCR

– Integrate multi-modal diffusion models for image, animation, and video generation

– Build video summarization, object tracking, and scene understanding pipelines using spatio-temporal AI

– Implement voice cloning, speech synthesis (TTS), and acoustic analytics using generative audio models

– Fine-tune and distill open-source and proprietary LLMs (e.g., LLaMA, Mistral, GPT, Claude) for compliance, legal, health, and finance domains

– Implement function calling, tool augmentation, and persona modeling for business-centric AI agents

– Apply RAG frameworks for private knowledge retrieval and custom Q&A systems

– Use prompt engineering, few-shot learning, and control tokens to guide model behavior in production environments

– Architect cloud-native environments for scalable model training, deployment, and monitoring (AWS SageMaker, Azure ML, Google AI, Vertex AI)

– Set up containerized inference (Docker, Kubernetes, Ray Serve) and serverless model endpoints

– Automate CI/CD pipelines for ML workflows with versioning, rollback, and A/B testing

– Implement observability stacks (Prometheus, Grafana, Evidently) to detect drift, bias, and performance anomalies

– Architect AI-driven mobile/web apps that incorporate real-time personalization, intelligent recommendations, and dynamic content generation

– Integrate on-device AI with edge inference (Core ML, TensorFlow Lite, ONNX) for low-latency and privacy-preserving experiences

– Build AI-native UX features: predictive search, natural conversation interfaces, vision-based input recognition, and behavioral analytics

– Optimize AI workflows across front-end and backend layers, enabling seamless user-AI collaboration at scale

– Engineer bespoke AI modules for integration with SAP, Oracle, Microsoft Dynamics, and NetSuite ERPs

– Implement intelligent document processing (IDP), invoice classification, and anomaly detection across financial, HR, and supply chain data

– Build AI middleware and APIs that bridge LLM agents with CRM, WMS, and SCM systems for contextual insights and decision support

– Apply RPA + AI for end-to-end intelligent workflows across procurement, compliance, and customer service

– Source and onboard AI-fluent engineers, data scientists, and research assistants trained in the latest model architectures and frameworks

– Build offshore pods embedded with DevOps, MLOps, LLMOps, and AI QA for end-to-end delivery ownership

– Operate under robust governance frameworks including ISO 27001, SOC 2, and GDPR-compliance for secure global collaboration

– Provide continuous training and mentorship pipelines to ensure teams stay aligned with evolving AI industry standards and tooling