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GeekyAnts India Pvt Ltd

Services

251 - 500

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4.5

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Bengaluru, Karnataka

Location

About company

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

GeekyAnts has worked with around 500+ clients all across the globe, delivering tailored solutions to a wide array of industries like Healthcare, Finance, Education, Banking, Gaming, Manufacturing, Real Estate and more. They are trusted tech partners of some of the world's top corporate giants and have helped small to mid-sized companies realize their vision and transform digitally. They are also the registered service suppliers 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.

In addition to that, GeekyAnts is the brains behind React Native's most famous UI library; NativeBase (15000+ GitHub Stars), BuilderX, Vue Native, Flutter Starter, apibeats and hold numerous other Open Source contributions to their name. GeekyAnts has offices in India (Bangalore) and the UK (London)

Senior AI/ML Engineer - I

Posted a month ago

Not Disclosed

Salary

5-7 years

Experience

Bengaluru, Karnataka

Location

Job Description

We’re hiring a Senior AI/ML Engineer to lead the design, optimization, and deployment of advanced AI systems. This role goes beyond integration — you’ll architect, fine-tune, and scale LLMs, vision, and speech models, while guiding junior engineers and influencing the AI roadmap for GeekyAnts. You’ll work across core ML/DL, 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’ AIfootprint.

    • Educational Qualifications

    Educational Background

  • 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.

    • Rounds description

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