Senior Software Engineer

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 optimisation, ensuring production-grade reliability, explainability, and innovation.

Key Responsibilitie

sArchitecture & System Desig

nArchitect 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).Optimise model architectures for inference performance, quantisation, 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 anonymisation using Presidio or custom filters.

AI Ops & Deployment: Automate model workflows with MLflow, Kubeflow, or Vertex AI for experiment tracking and version

ing.Lead model deployment with vLLM, TGI, or TorchServe, ensuring optimised GPU/TPU utilisation. 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 datas

ets.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 an AI road

maps.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, Llama

Index.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, quantisation, and mixed-precision optimisation.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 optimisation, or multimodal rea

soning.Understanding of AI cost governance, observability, and MLOps automation.

Soft SkillsLeadership 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 collaborationhabits.

What You'll Build

Enterprise-scale RAG and Agentic Systems across domains and mo

dalities.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.