Urgent requirement For AI/ML Engineer.

Required skills:

• 5-8 years of hands-on experience in AI, machine learning, and NLP-focused roles.
• Proven ability to build, fine-tune, and deploy LLM-based solutions in production environments.
• Excellent analytical, problem-solving, and communication skills.
• Strong attention to detail and commitment to delivering high-quality solutions.
• Proficiency in Python for data analysis, LLM fine-tuning, and automation.
• Hands-on experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Expertise in large language models (LLMs) such as ChatGPT, Gemini, LLaMA, and open-source alternatives.
• Experience in leveraging APIs of LLM platforms like OpenAI, Google Gemini, and Meta AI.
• Familiarity with tools for LLM fine-tuning, reinforcement learning from human feedback (RLHF), and custom deployment.
• Strong knowledge of NLP techniques and frameworks (e.g., Hugging Face Transformers, spaCy, NLTK).
• Experience in OCR tools and libraries (e.g., Tesseract, Google OCR) for intelligent document processing.
• Familiarity with chatbot frameworks (e.g., LangChain, Haystack, Dialogflow) and web scraping tools (e.g., BeautifulSoup, Scrapy, Playwright).
• Knowledge of cloud platforms (e.g., GCP, AWS, Azure) for deploying and managing AI and LLM solutions.
• Strong understanding of MLOps tools (e.g., MLflow, Kubeflow, Vertex AI) for model management and pipeline automation.

Key Responsibilities:

• Design and implement machine learning models and algorithms for structured and unstructured data.
• Develop and deploy intelligent document processing solutions to extract, validate, and structure data.
• Build and enhance conversational AI frameworks using natural language processing (NLP) techniques and large language models (LLMs) such as ChatGPT, Gemini, and LLaMA.
• Integrate LLMs into applications for contextual understanding, content generation, and decision-making processes.
• Automate end-to-end workflows, including chatbot development, data extraction, and system integration, to improve operational efficiency.
• Collaborate with cross-functional teams to understand business requirements and translate them into LLM-powered solutions.
• Ensure AI/ML and LLM-based solutions are scalable, reliable, and compliant with security and privacy standards.
• Continuously monitor and optimize the performance of deployed LLMs and AI/ML systems, including fine-tuning and retraining as needed.
• Research and integrate state-of-the-art advancements in LLMs and AI/ML into existing and new projects.
• Document workflows, methodologies, and best practices for LLM and AI model usage to promote knowledge sharing and scalability.