We Promise You An Inclusive Work Environment AI/ML Engineer- ML02 Job Description AI/ML Engineer – LLMs, RAG, Reinforcement Learning Experience: Not required Employment Type: Full-time Studies : Mtech or PhD About the Role We are building enterprise-grade AI/ML solutions including SLMs, LLMs, RAG-based knowledge systems, reinforcement learning, and agentic AI, As a Mid-level AI/ML Engineer, you will design, train, and deploy machine learning models, collaborate with our product and engineering teams, and ensure scalable integration of AI models into real-world applications. This role is ideal for someone with a strong hands-on background in NLP, deep learning, and reinforcement learning, who is eager to grow by working on cutting-edge AI projects at scale. Key Responsibilities Design, train, and fine-tune ML/DL models (with focus on transformers, SLMs, LLMs, and recommender systems). Implement RAG pipelines using vector databases (Pinecone, Weaviate, FAISS) and frameworks like LangChain or LlamaIndex. Contribute to LLM fine-tuning using LoRA, QLoRA, and PEFT techniques. Work on reinforcement learning (RL/RLHF) for optimizing LLM responses. Build data preprocessing pipelines for structured and unstructured datasets. Collaborate with backend engineers to expose models as APIs using FastAPI/Flask. Ensure scalable deployment using Docker, Kubernetes, AWS/GCP/Azure ML services. Monitor and optimize model performance (latency, accuracy, hallucination rates). Use MLflow / Weights & Biases for experiment tracking and versioning. Stay updated with the latest research papers and open-source tools in AI/ML. Contribute to code reviews, technical documentation, and best practices. Required Skills & Qualifications Strong in Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow). Solid understanding of NLP and LLM architectures (Transformers, BERT, GPT, LLaMA, Mistral). Practical experience with vector databases (Pinecone, or FAISS or PgVector). Basic Knowledge with MLOps tools – Docker, Kubernetes, MLflow, CI/CD. Basic Knowledge of cloud platforms (AWS Sagemaker, GCP Vertex AI, or Azure ML). Good grasp of linear algebra, probability, statistics, optimization. Strong debugging, problem-solving, and analytical skills. Familiarity with Agile methodologies (Scrum, Jira, Git). Nice-to-Have Skills Experience with RLHF pipelines. Open-source contributions in AI/ML. Soft Skills Strong communication – able to explain AI concepts to technical & non-technical stakeholders. Collaborative – works well with product, design, and engineering teams. Growth mindset – eager to learn new AI techniques and experiment. Accountability – able to deliver end-to-end model pipelines with minimal supervision. Can works in a team. What We Offer Work on cutting-edge AI projects with real-world enterprise impact. Exposure to LLMs, reinforcement learning, and agentic AI. Collaborative Startup & Service culture with room for fast growth. Competitive compensation + performance-based incentives.
As an AI/ML Engineer at our company, you will be responsible for designing, training, and deploying machine learning models. Your role will involve collaborating with product and engineering teams to ensure scalable integration of AI models into real-world applications. This position is ideal for individuals with a strong background in NLP, deep learning, and reinforcement learning, who are eager to work on cutting-edge AI projects at scale. **Key Responsibilities:** - Design, train, and fine-tune ML/DL models with a focus on transformers, SLMs, LLMs, and recommender systems. - Implement RAG pipelines using vector databases such as Pinecone, Weaviate, FAISS, and frameworks like LangChain or LlamaIndex. - Contribute to LLM fine-tuning using LoRA, QLoRA, and PEFT techniques. - Work on reinforcement learning (RL/RLHF) to optimize LLM responses. - Build data preprocessing pipelines for structured and unstructured datasets. - Collaborate with backend engineers to expose models as APIs using FastAPI/Flask. - Ensure scalable deployment using Docker, Kubernetes, and AWS/GCP/Azure ML services. - Monitor and optimize model performance in terms of latency, accuracy, and hallucination rates. - Utilize MLflow and Weights & Biases for experiment tracking and versioning. - Stay updated with the latest research papers and open-source tools in AI/ML. - Contribute to code reviews, technical documentation, and best practices. **Required Skills & Qualifications:** - Strong proficiency in Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow). - Solid understanding of NLP and LLM architectures including Transformers, BERT, GPT, LLaMA, and Mistral. - Practical experience with vector databases such as Pinecone, FAISS, or PgVector. - Basic knowledge of MLOps tools like Docker, Kubernetes, MLflow, and CI/CD. - Familiarity with cloud platforms such as AWS Sagemaker, GCP Vertex AI, or Azure ML. - Good grasp of linear algebra, probability, statistics, and optimization. - Strong debugging, problem-solving, and analytical skills. - Familiarity with Agile methodologies like Scrum, Jira, and Git. **Nice-to-Have Skills:** - Experience with RLHF pipelines. - Open-source contributions in AI/ML. **Soft Skills:** - Strong communication skills to explain AI concepts to technical and non-technical stakeholders. - Collaborative nature to work effectively with product, design, and engineering teams. - Growth mindset with a willingness to learn new AI techniques and experiment. - Accountability to deliver end-to-end model pipelines with minimal supervision. - Ability to work effectively in a team environment. Join us to work on cutting-edge AI projects with real-world enterprise impact, exposure to LLMs, reinforcement learning, and agentic AI, a collaborative startup culture with rapid growth opportunities, and competitive compensation with performance-based incentives.,