Senior AI/ML Engineer

About GeekyAnts

GeekyAnts is a design and development studio that specializes in building solutions for web and mobile, driving innovation and transforming industries and lives. They hold expertise in state-of-the-art technologies like React, React Native, Flutter, Angular, Vue, NodeJS, Python, and Svelte.

GeekyAnts has worked with over 500+ clients globally, delivering tailored solutions to industries such as Healthcare, Finance, Education, Banking, Gaming, Manufacturing, and Real Estate. They are trusted tech partners to top corporate giants and have helped small to mid-sized companies realize their vision. GeekyAnts has been a registered service supplier for Google LLC since 2017.

They provide services ranging from Web & Mobile Development, UI/UX design, Business Analysis, Product Management, DevOps, QA, API Development, Delivery & Support, and more.

GeekyAnts is also known for creating React Native's NativeBase (15000+ GitHub Stars), BuilderX, Vue Native, Flutter Starter, and apibeats, alongside numerous other Open Source contributions. GeekyAnts maintains offices in India (Bangalore) and the UK (London).

The Senior AI/ML Engineer Role at GeekyAnts

We are hiring a Senior AI/ML Engineer to lead the design, optimization, and deployment of advanced AI systems. This role extends beyond integration, focusing on architecting, fine-tuning, and scaling Large Language Models (LLMs), vision, and speech models. You will also guide junior engineers and significantly influence the AI roadmap for GeekyAnts. The position involves working across core Machine Learning/Deep Learning, Retrieval Augmented Generation (RAG) systems, AI in Robotics/IoT, and inference optimization, ensuring production-grade reliability, explainability, and innovation.

Key Responsibilities

Architecture & System Design

• Architect and deploy end-to-end AI systems, from data pipelines to model serving.
• Design modular SDKs for multi-provider AI integration (OpenAI, Claude, Gemini, LLaMA).
• Lead decision-making on cloud vs self-hosted LLM deployment (Ollama, vLLM, TGI).
• Guide infrastructure design for scalability, observability, and cost efficiency using GPU clusters, Ray, or KServe.
• Collaborate with backend, MLOps, and infra teams to ensure high availability and low latency across AI workloads.

Core ML / DL Development

• Train and fine-tune models (CNN, RNN, Transformers) across text, vision, and speech domains.
• Implement LoRA / PEFT fine-tuning for custom LLMs, embedding models, and instruction-tuned variants.
• Work with open-source and proprietary model repositories (Hugging Face, Kaggle, Hugging Face Spaces).
• Optimize model architectures for inference performance, quantization, and memory efficiency.
• Conduct A/B testing, cross-validation, and human evaluation on model outputs.
• Build internal evaluation benchmarks and dataset management pipelines for consistent model scoring and comparison.

Data & Dataset Engineering

• Curate, clean, and version-control datasets for text, image, and audio modalities.
• Build pipelines for data labelling, augmentation, and validation using Airflow / Prefect.
• Create and manage feature stores, embedding repositories, and dataset registries.
• Leverage open datasets (e.g., Common Crawl, LAION, OpenImages, LibriSpeech) and integrate custom enterprise datasets.
• Ensure data governance, bias checks, and PII anonymization using Presidio or custom filters.

AI Ops & Deployment

• Automate model workflows with MLflow, Kubeflow, or Vertex AI for experiment tracking and versioning.
• Lead model deployment with vLLM, TGI, or TorchServe, ensuring optimized GPU/TPU utilization.
• Set up continuous evaluation pipelines for model drift, bias, and quality decay using EvidentlyAI and Prometheus.
• Leverage open datasets (e.g., Common Crawl, LAION, OpenImages, LibriSpeech) and integrate custom enterprise datasets.
• Drive adoption of model registries and model cards for transparency and reproducibility.

Team & Technical Leadership

• Mentor and review the work of AI/ML Engineers I & II.
• Collaborate with product, design, and research teams to translate business needs into AI roadmaps.
• Lead POCs and experiments for emerging AI verticals (e.g., multimodal, video, robotics, IoT intelligence).
• Present internal demos, AI reports, and architectural documentation to leadership and clients.

Core Skills Required

• Programming: Expert-level Python, with a deep understanding of OOP, async, and design patterns.
• Frameworks: PyTorch, TensorFlow, Hugging Face Transformers, LangChain, LlamaIndex.
• Model Ops: MLflow, KServe, TorchServe, vLLM, TGI.
• Data Stack: Airflow / Prefect, pgvector, Milvus, Pinecone, FOSS, PostgreSQL.
• Infra: Docker, Kubernetes, Ray, GPU servers, Cloud AI (Vertex AI, Bedrock, Azure).
• Evaluation & Metrics: Familiarity with BLEU, ROUGE, and latency/throughput metrics for AI models.
• Security: Secure Vaults, Microsoft Presidio, Fairlearn / AIF360 awareness for data and bias governance.

Good-to-Have Skills

• Experience with distributed training, quantization, and mixed-precision optimization.
• Experience with model compression, distillation, or low-rank adaptation for efficiency.
• Contribution to open-source AI frameworks or Hugging Face Spaces.
• Research exposure in LLM alignment, prompt optimization, or multimodal reasoning.
• Understanding of AI cost governance, observability, and MLOps automation.

Soft Skills

• Leadership and mentorship mindset with strong communication skills.
• Strategic thinker with the ability to drive architectural decisions.
• Ownership-driven approach to solving complex AI problems.
• Strong documentation and cross-team collaboration habits.

What You’ll Build

• Enterprise-scale RAG and Agentic Systems across domains and modalities.
• Self-hosted AI stack for multi-modal intelligence (text, image, voice).
• Reusable AI SDKs, dataset registries, and model inference frameworks powering the GeekyAnts AI ecosystem.
• Open-source contributions and internal model spaces that expand GeekyAnts’ AI footprint.

Educational Qualifications

• Bachelor’s or Master’s in Computer Science, Data Science, or related fields.
• Advanced certifications or research exposure in AI/ML/DL is an added advantage.

Interview Process

The hiring process for the Senior AI/ML Engineer at GeekyAnts includes:

• Automated Video Call: An approximate 30-minute automated video call. Candidates should be ready with their resume, ensure a stable internet connection, and will be evaluated on past experience, design skills, and knowledge.
• One-to-One In-person Interview with CEO: A direct interview with the Chief Executive Officer for Technical Assessments & Review.
• One-to-One In-person Interview with HR: A direct interview with the HR of GeekyAnts.

Key skills/competency

• AI System Architecture
• Large Language Models (LLMs)
• Machine Learning (ML)
• Deep Learning (DL)
• MLOps
• Python Programming
• PyTorch/TensorFlow
• Data Engineering
• Inference Optimization
• Team Leadership