AI Engineer

Namaskaram!

AjnaLens is looking for an AI/ML Engineer to join our Product Engineering team at Thane (Maharashtra – India). The ideal candidate should have 5+ years of experience building, integrating, optimizing, and deploying AI systems in real-world production environments. The role focuses on applying modern AI/ML techniques across Generative AI, LLMs, Computer Vision, multimodal AI, inference optimization, AI agents, and edge AI. Candidates should be comfortable taking AI solutions from experimentation and prototyping through production deployment, monitoring, and optimization. This role demands a strong product-focused mindset, practical engineering skills, and the ability to translate cutting-edge AI capabilities into reliable, scalable, and efficient products.

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Who are we looking for:

We are looking for a highly skilled AI/ML Engineer with strong hands-on experience in building, integrating, evaluating, and deploying AI solutions across Generative AI, LLMs, Computer Vision, multimodal AI, and AI agents. The candidate should have a solid foundation in machine learning and deep learning concepts, including supervised and unsupervised learning, model evaluation, neural network architectures, optimization, and transfer learning. The ideal candidate should understand the complete AI lifecycle—from data preparation and experimentation to model training/fine-tuning, evaluation, model integration, inference optimization, API/model serving, deployment, monitoring, and production support. Experience with LLM fine-tuning, RAG, prompt engineering, model quantization, inference optimization, and deploying AI workloads on GPUs/VMs is highly valuable. Strong Python skills and working knowledge of C++ for performance-critical applications are expected.

Top 3 Daily Tasks:

Build, integrate, fine-tune, and optimize AI models and AI-powered features across Generative AI, LLM, Computer Vision, multimodal, and agentic AI use cases.

Develop and deploy production-grade AI services, inference pipelines, and model-serving APIs using technologies such as FastAPI, Docker, GPU runtimes, and cloud/on-premise VMs.

Evaluate, monitor, troubleshoot, and optimize AI systems in production, focusing on latency, throughput, resource utilization, reliability, model quality, and cost.

Minimum work experience is required:

Minimum 5 years of hands-on experience in AI/ML engineering, production AI systems, model integration/deployment, and the end-to-end lifecycle of AI-powered applications.

Top 5 Skills you should possess:

Strong proficiency in Python and solid working knowledge of C++ for AI integration, performance-critical systems, and inference optimization

Strong understanding of Machine Learning and Deep Learning fundamentals, including model selection, feature engineering, supervised/unsupervised learning, neural network architectures, CNNs, transformers, transfer learning, training/validation, and model evaluation; hands-on experience with PyTorch or similar frameworks

Hands-on experience with Generative AI, LLMs, LLM fine-tuning, prompt engineering, RAG, embeddings, vector databases, AI agents, and/or multimodal AI

Practical experience with AI inference optimization, including quantization, model compression, batching, GPU utilization, latency/throughput optimization, and model serving

Strong understanding of AI deployment, MLOps, model monitoring, API design, Docker, Linux, GPU-based infrastructure, and deploying AI workloads on VMs/cloud environments

Preferred / Good to Have:

Experience with classical ML techniques such as regression, classification, clustering, feature engineering, ensemble methods, and model evaluation

Practical experience with deep learning architectures such as CNNs, RNNs/LSTMs, transformers, vision transformers, and transfer learning

Experience with training and fine-tuning models, hyperparameter optimization, experiment tracking, dataset versioning, and reproducible ML workflows

What would you be expected to do:

Experience deploying and managing AI inference workloads on Linux VMs, GPU VMs, cloud infrastructure, or on-premise servers

Familiarity with inference runtimes and optimization stacks such as ONNX Runtime, TensorRT, vLLM, LiteRT/LiteRT-LM, or similar technologies

Experience with multimodal AI, vision-language models, speech/audio AI, or AI systems integrated with hardware products

Working knowledge of Kubernetes, CI/CD, Docker, observability, and production infrastructure for AI workloads

Experience with C++, CUDA, GPU profiling, or hardware-aware optimization is an added advantage