Software Engineer- AI/ML

Job Description

We are seeking an experienced AI Engineer with 4-5 years of hands-on experience in designing and implementing AI solutions. The ideal candidate should have a strong foundation in developing AI/ML-based solutions, including expertise in Computer Vision (OpenCV). Additionally, proficiency in developing, fine-tuning, and deploying Large Language Models (LLMs) is essential.

As an AI Engineer, candidate will work on cutting-edge AI applications, using LLMs like GPT, LLaMA, or custom fine-tuned models to build intelligent, scalable, and impactful solutions. candidate will collaborate closely with Product, Data Science, and Engineering teams to define, develop, and optimize AI/ML models for real-world business applications.

Key Responsibilities

• Research, design, and develop AI/ML solutions for real-world business applications, RAG is must.
• Collaborate with Product & Data Science teams to define core AI/ML platform features.
• Analyze business requirements and identify pre-trained models that align with use cases.
• Work with multi-agent AI frameworks like LangChain, LangGraph, and LlamaIndex.
• Train and fine-tune LLMs (GPT, LLaMA, Gemini, etc.) for domain-specific tasks.
• Implement Retrieval-Augmented Generation (RAG) workflows and optimize LLM inference.
• Develop NLP-based GenAI applications, including chatbots, document automation, and AI agents.
• Preprocess, clean, and analyze large datasets to train and improve AI models.
• Optimize LLM inference speed, memory efficiency, and resource utilization.
• Deploy AI models in cloud environments (AWS, Azure, GCP) or on-premises infrastructure.
• Develop APIs, pipelines, and frameworks for integrating AI solutions into products.
• Conduct performance evaluations and fine-tune models for accuracy, latency, and scalability.
• Stay updated with advancements in AI, ML, and GenAI technologies.
  
  

Required Skills & Experience

• AI & Machine Learning: Strong experience in developing & deploying AI/ML models.
• Generative AI & LLMs: Expertise in LLM pretraining, fine-tuning, and optimization.
• NLP & Computer Vision: Hands-on experience in NLP, Transformers, OpenCV, YOLO, R-CNN.
• AI Agents & Multi-Agent Frameworks: Experience with LangChain, LangGraph, LlamaIndex.
• Deep Learning & Frameworks: Proficiency in TensorFlow, PyTorch, Keras.
• Cloud & Infrastructure: Strong knowledge of AWS, Azure, or GCP for AI deployment.
• Model Optimization: Experience in LLM inference optimization for speed & memory efficiency.
• Programming & Development: Proficiency in Python and experience in API development.
• Statistical & ML Techniques: Knowledge of Regression, Classification, Clustering, SVMs, Decision Trees, Neural Networks.
• Debugging & Performance Tuning: Strong skills in unit testing, debugging, and model evaluation.
• Hands-on experience with Vector Databases (FAISS, ChromaDB, Weaviate, Pinecone).
  
  

Good To Have

• Experience with multi-modal AI (text, image, video, speech processing).
• Familiarity with containerization (Docker, Kubernetes) and model serving (FastAPI, Flask, Triton).
  
  

Requirements

We are seeking an experienced AI Engineer with 4-5 years of hands-on experience in designing and implementing AI solutions. The ideal candidate should have a strong foundation in developing AI/ML-based solutions, including expertise in Computer Vision (OpenCV). Additionally, proficiency in developing, fine-tuning, and deploying Large Language Models (LLMs) is essential. As an AI Engineer, candidate will work on cutting-edge AI applications, using LLMs like GPT, LLaMA, or custom fine-tuned models to build intelligent, scalable, and impactful solutions. candidate will collaborate closely with Product, Data Science, and Engineering teams to define, develop, and optimize AI/ML models for real-world business applications. Key Responsibilities: - Research, design, and develop AI/ML solutions for real-world business applications, RAG is must. - Collaborate with Product & Data Science teams to define core AI/ML platform features. - Analyze business requirements and identify pre-trained models that align with use cases. - Work with multi-agent AI frameworks like LangChain, LangGraph, and LlamaIndex. - Train and fine-tune LLMs (GPT, LLaMA, Gemini, etc.) for domain-specific tasks. - Implement Retrieval-Augmented Generation (RAG) workflows and optimize LLM inference. - Develop NLP-based GenAI applications, including chatbots, document automation, and AI agents. - Preprocess, clean, and analyze large datasets to train and improve AI models. - Optimize LLM inference speed, memory efficiency, and resource utilization. - Deploy AI models in cloud environments (AWS, Azure, GCP) or on-premises infrastructure. - Develop APIs, pipelines, and frameworks for integrating AI solutions into products. - Conduct performance evaluations and fine-tune models for accuracy, latency, and scalability. - Stay updated with advancements in AI, ML, and GenAI technologies. Required Skills & Experience: - AI & Machine Learning: Strong experience in developing & deploying AI/ML models. - Generative AI & LLMs: Expertise in LLM pretraining, fine-tuning, and optimization. - NLP & Computer Vision: Hands-on experience in NLP, Transformers, OpenCV, YOLO, R-CNN. - AI Agents & Multi-Agent Frameworks: Experience with LangChain, LangGraph, LlamaIndex. - Deep Learning & Frameworks: Proficiency in TensorFlow, PyTorch, Keras. - Cloud & Infrastructure: Strong knowledge of AWS, Azure, or GCP for AI deployment. - Model Optimization: Experience in LLM inference optimization for speed & memory efficiency. - Programming & Development: Proficiency in Python and experience in API development. - Statistical & ML Techniques: Knowledge of Regression, Classification, Clustering, SVMs, Decision Trees, Neural Networks. - Debugging & Performance Tuning: Strong skills in unit testing, debugging, and model evaluation. - Hands-on experience with Vector Databases (FAISS, ChromaDB, Weaviate, Pinecone). Good to Have: - Experience with multi-modal AI (text, image, video, speech processing). - Familiarity with containerization (Docker, Kubernetes) and model serving (FastAPI, Flask, Triton).