Experience : 8.00 + years Salary : USD 4074-4814 / month (based on experience) Expected Notice Period : 15 Days Shift : (GMT+05:30) Asia/Kolkata (IST) Opportunity Type : Remote Placement Type : Full Time Contract for 5 Months(40 hrs a week/160 hrs a month) (*Note: This is a requirement for one of Uplers' client - A leading US-based digital consultancy with a track record of excellence) What do you need for this opportunity? Must have skills required: FastAPI, Hugging Face, Knowledge graphs, MLOps, Quantization, TensorFlow, AI, ChatGPT, LLM Fine-tuning, Rag (retrieval-augmented generation), Vector databases, Python A leading US-based digital consultancy with a track record of excellence is Looking for: Role : Senior Python / AI Engineer: Hybrid (Mumbai) Experience : 6+years Work Location : Mumbai : Hybrid (One week in a month) Engagement : Contract To Hire (Initially 5 months to start) : Start date : Immediate Timing : 2pm to 11 pm IST Interview process: 2 Rounds(Aptitude round + Technical round ) Job Description : Overall Job Mission: To design, develop, implement, and optimize AI-driven solutions by effectively leveraging and integrating existing Large Language Models and related technologies. Outcomes (What does the person need to achieve?) LLM Integration & Application Development: Successfully integrate existing LLMs (e.g., GPT, LLaMA, Mistral, Claude, Gemini) into Python-based applications to deliver AI-powered features. (e.g., Develop and deploy 3 applications with LLM-driven functionality within the first 6 months with a user satisfaction rating of 4.5/5). Prompt Engineering & Optimization: Design, implement, and rigorously test prompts to maximize the effectiveness and accuracy of existing LLMs for specific application requirements. (e.g., Improve the accuracy of LLM-driven features by 20% through prompt engineering best practices). AI Solution Optimization: Optimize the performance, efficiency, and scalability of AI solutions built with LLMs, focusing on factors like response time, cost-effectiveness, and resource utilization. (e.g., Reduce the average response time of LLM-based applications by 15% while maintaining accuracy). Data Handling & Retrieval: Implement effective data processing, including preprocessing and cleaning of text datasets, and utilize vector databases to enable efficient information retrieval for LLM applications. (e.g., Achieve a 90% success rate in retrieving relevant information from vector databases for LLM queries). Deployment & Scalability: Deploy and scale LLM-powered applications on cloud platforms to support a growing user base and ensure high availability. (e.g., Successfully scale LLM applications to handle a 50% increase in user traffic without performance degradation). Competencies (How does the person need to behave?) LLM Application Expertise: Possesses strong skills in integrating and applying existing LLMs through APIs and libraries, with a focus on prompt engineering and application development. Python Development & AI Frameworks: Demonstrates proficiency in Python programming and AI/ML frameworks (Hugging Face, PyTorch, TensorFlow) for building and deploying LLM-based solutions. Problem Solving & Adaptability: Exhibits the ability to solve challenges related to LLM integration, optimize performance, and adapt to the evolving landscape of LLM technologies. Collaboration & Communication: Effectively communicates technical solutions and collaborates with cross-functional teams to deliver impactful AI applications. Results Orientation: Focuses on delivering functional, efficient, and scalable AI solutions that meet business needs and user expectations. Required Skills & Experience- Must-Have Hands-on Experience: Python programming with AI/ML frameworks (Hugging Face, PyTorch, TensorFlow). Hands-on experience working with LLMs and fine-tuning. Experience in prompt engineering and optimizing AI model outputs. Building APIs with FastAPI or Flask for AI model integration. Familiarity with vector databases and embedding models. Experience with LangChain, LlamaIndex, or Retrieval-Augmented Generation (RAG). Nice to Have (or Learn on the Job): Knowledge of quantization techniques (LoRA, GPTQ, vLLM, ONNX) for efficient model deployment. Experience working with knowledge graphs and reasoning-based AI. Background in MLOps for tracking and managing AI models. How to apply for this opportunity? Step 1: Click On Apply! And Register or Login on our portal. Step 2: Complete the Screening Form & Upload updated Resume Step 3: Increase your chances to get shortlisted & meet the client for the Interview! About Uplers: Our goal is to make hiring reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant contractual onsite opportunities and progress in their career. We will support any grievances or challenges you may face during the engagement. (Note: There are many more opportunities apart from this on the portal. Depending on the assessments you clear, you can apply for them as well). So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you! Show more Show less
