216 Rag Jobs - Page 3

Roles and Responsibilities : Lead a team of data scientists to design, develop, and deploy machine learning models using Python and natural language processing techniques. Collaborate with cross-functional teams to identify business problems that can be solved through data analysis and modeling. Develop and maintain complex algorithms for text classification, sentiment analysis, and topic modeling. Ensure high-quality deliverables by reviewing code reviews, providing feedback on technical designs, and mentoring junior team members. Job Requirements : 7-13 years of experience in machine learning or related field (data science). Strong proficiency in Python programming language with expertise in libraries such as scikit-learn, TensorFlow, or PyTorch. Experience with natural language processing techniques including text preprocessing, feature extraction, and model evaluation metrics.

Key Responsibilities: Design, develop, and train deep learning models for structured and unstructured data. Fine-tune pre-trained large language models (e.g., LLaMA, Mistral, Falcon, GPT-based models) for custom use cases. Evaluate model performance using standard metrics and improve based on experimentation. Build and maintain scalable training pipelines and inference systems. Collaborate with product and research teams to convert business requirements into technical solutions. Perform data preprocessing, augmentation, and annotation where needed. Optimize model inference speed and resource efficiency (e.g., quantization, pruning). Keep up with the latest research in ML/DL and apply relevant techniques to real-world problems. Required Qualifications: B.Tech / M.Tech / MS in Computer Science, Electrical Engineering, or related field. 23 years of hands-on experience in machine learning and deep learning. Strong knowledge of Python and frameworks such as PyTorch or TensorFlow. Proven experience training deep neural networks from scratch or via transfer learning. Exposure to LLMs – fine-tuning, prompt engineering, or training smaller transformer models. Familiarity with deployment of models using FastAPI, Flask, or similar. Preferred Qualifications: Experience with Hugging Face Transformers, LangChain, or similar frameworks. Knowledge of distributed training and GPU acceleration. Understanding of vector databases and RAG (retrieval-augmented generation) systems. Basic understanding of MLOps tools like Docker, Kubernetes, and CI/CD for ML workflows. Familiarity with common NLP tasks: text classification, summarization, NER, etc. What We Offer: A fast-paced, collaborative, and intellectually challenging environment. Opportunity to work on cutting-edge AI products and infrastructure. Flexible work options and a strong focus on work-life balance. Competitive compensation and equity options (if applicable).

Meet the Team Our Licensing Experience and Operation team, collaborates with multi-functional collaborators to provide and handle the capabilities required for on-premises software, license and documentation setup and delivery, provisioning and device activation. Our service is key to achieving Ciscos Recurring Revenue strategies, handling software and licensing distribution to ALL routes to market. The role also involves extensive cross organization collaboration with internal collaborators such as Business Units, IT Architects and Program Managers. Your Impact You are a motivated and agile self-starter with the ability to drive multiple complex and sometimes ambiguous programs to completion. Proficient in project management tools and methodologies, you are confident in facilitating effective meetings and preparing and communicating project analysis, status updates, challenges, and other key insights that resonate with executive leadership. You thrive in team environments, demonstrating clear and concise communication skills, active listening, and effective questioning techniques to resolve issues. Resourceful and adaptable, you excel at handling or leading project teams of varying sizes and diverse abilities to achieve optimal results, handling interdependencies and leading horizontally across functions. You have experience communicating with senior management to drive alignment among business partners from diverse functions, writing detailed specifications of user requirements, business processes, and procedures for implementing software solutions, and leading projects effectively. With a strong desire to learn and work independently in a fast-paced, high-growth environment, you also collaborate effectively with cross-functional teams of business analysts and developers while maintaining meticulous attention to detail. Minimum Qualifications : 4-7 years of demonstrated experience, including: Experience in Program and Product Management: Demonstrated ability to lead end-to-end programs and products, including defining strategy, planning, execution, and delivery, while ensuring alignment with collaborators, meeting deadlines, and achieving business objectives Experience in AI/Automation Projects: Hands-on experience leading programs or products in the AI or automation domain, with a focus on delivering value-driven solutions Requirements Gathering and Documentation: Strong skills in gathering, analyzing, and writing detailed business requirements and user stories to drive product and project outcomes. Data Analysis Skills: Ability to analyze AI model and project-related data to identify trends, areas of improvement, measure success, and inform decision-making. Strong Communication and Collaboration Skills: Excellent ability to connect with cross-functional teams, technical partners, and leadership, with a focus on clear and concise documentation and presentations. Preferred Qualifications : Knowledge of Emerging Technologies: Familiarity with AI/ML concepts (e.g. Agentic framework, RAG/LLM models), automation tools (e.g., AI Assistants), with the ability to bridge the gap between technical teams and business needs.

Hello, Greetings from GlobalData!!! Job Opening for below roles @ GlobalData -Hyderabad. Data Science Senior Data Science Job Criteria :- Qualification: B.Tech/BE/MCA/M.Tech/B.Sc/M.Sc-(Computers) Experience Range : 2 to 5 years Key Skills : Python, SQL, Gen AI, LLM, Data Science Notice Period : Immediate joiners or with one month notice period Job Location : Hyderabad Work Mode : Hybrid Salary : best as per market standard's Detailed job description given below please go through to understand requirement. If requirement is matching to your profile share your updated resume @ mail id (m.salim@globaldata.com). Mention Subject Line :-Applying for Data Science @ GlobalData-Hyd. Please Share your details in the mail :- Your Full Name : Mobile # : Qualification : Company Name : Designation : Total Work Experience Years : Current CTC : Expected CTC : Notice Period : Present Location : If out-station willing to relocate to Hyd : Yes/No Job Description:- Data Scientist Location: Hyderabad, India Experience: 2+ years About the Role :- We are looking for a Data Scientist with at least 2 years of experience to join our Hyderabad team. The ideal candidate is skilled in Python and SQL, with a strong analytical mindset. Experience with Generative AI (GenAI) or Large Language Models (LLMs) is a plus. Responsibilities Analyze large datasets and extract actionable insights. Build and deploy machine learning models. Write efficient SQL queries for data extraction and analysis. Collaborate with cross-functional teams to deliver data-driven solutions. Present findings to technical and non-technical stakeholders. Requirements Bachelors or Masters in Computer Science, Statistics, Mathematics, Engineering, or related field. 2+ years of experience in Data Science or a similar role. Strong Python and SQL skills. Solid understanding of statistics, machine learning, and data visualization. Excellent problem-solving and communication skills. Preferred Experience with GenAI or LLMs. Familiarity with cloud platforms (AWS, GCP, Azure) and big data tools (Spark, Hadoop). Knowledge of deep learning frameworks (TensorFlow, PyTorch). Job Description: Senior Data Scientist Location: Hyderabad, India Experience: 5+ years About the Role :- We are seeking a Senior Data Scientist with at least 5 years of experience to join our Hyderabad team. The ideal candidate has strong expertise in Python and SQL, hands-on experience with Generative AI (GenAI) or Large Language Models (LLMs), and a proven ability to manage multiple projects. You will lead data science initiatives, ensure timely project delivery, and mentor junior team members. Responsibilities Lead and deliver multiple data science projects on schedule. Design, develop, and deploy advanced machine learning and GenAI/LLM solutions. Guide and mentor junior data scientists, providing technical direction and support. Collaborate with cross-functional teams to define project goals and deliverables. Communicate complex findings clearly to stakeholders at all levels. Requirements Bachelors or Masters in Computer Science, Statistics, Mathematics, Engineering, or related field. 5+ years of experience in Data Science or a similar role. Strong proficiency in Python and SQL. Hands-on experience with GenAI or LLMs. Proven project management and team leadership skills. Deep understanding of statistics, machine learning, and data visualization. Excellent problem-solving, communication, and organizational skills. Preferred Experience with handling data formats like structured (SQL), semi-structured (Elasticsearch) and unstructured (PDF, Images in S3) Experience with cloud platforms (AWS, GCP, Azure) and big data tools (Spark, Hadoop). Knowledge of deep learning frameworks (TensorFlow, PyTorch). Thanks & Regards, Salim (Human Resources)

Job Title: AI Developer Supply Chain Management Location: Permanent Remote Job Type: Full-time Team: Product AI Engineering We are looking for a talented LLM Developer to join our AI Engineering team and contribute to the development of the SCM Expert AI Agent the core brain behind AI capabilities like demand forecasting, anomaly detection, ETA prediction, and route optimization in the FarmToPlate (F2P) platform. In this role, you will focus on integrating and optimizing LLM-based agentic architectures using frameworks like LangChain, CrewAI, and RAG pipelines. Youll work closely with traditional ML developers and backend engineers to deploy intelligent, explainable, and real-time decision agents for supply chain management. Responsibilities: Design, build, and optimize LLM-based agents using LangChain, CrewAI, or custom wrappers. Implement RAG pipelines with vector stores (e.g., FAISS, ChromaDB) and embedding models. Develop LLM-powered tools for demand forecasting, query understanding, anomaly explanations, and summarization. Integrate agents with internal and external data sources using tools like MongoDB, Sklearn datasets, or Browsing Agents. Implement prompt engineering, agent memory, and context-chaining for more dynamic agent behavior. Collaborate with data science and product teams to translate SCM logic into agentic workflows. Expose models and agents through APIs using FastAPI, including role-based access if needed. Optimize runtime performance with quantization, PEFT (parameter-efficient finetuning), and model caching. Track experiments, versions, and deployment artifacts using tools like MLflow, SageMaker, or custom registries. Requirements: 3-5 years of experience working on Python-based AI systems. Strong understanding of LLMs, Transformers, Prompt Engineering, and conversational AI. Hands-on experience with LangChain, CrewAI, OpenAI/GPT APIs, or Hugging Face Transformers. Familiarity with RAG concepts, vector databases (FAISS, Chroma), and embedding techniques. Good knowledge of API frameworks (FastAPI/Flask) and working with JSON schema-driven inputs. Basic understanding of traditional ML models and workflows (e.g., regression, classification, anomaly detection). Comfortable integrating external data tools and APIs into LLM pipelines. Bonus Points For Experience building Agentic RAG systems that combine logic, tools, memory, and models. Knowledge of Open Source LLM tuning using PEFT, LoRA, bitsandbytes, Unsloth, etc. Familiarity with SageMaker, EKS, or deploying custom models to the cloud. Experience in supply chain/logistics, or working with ERP/SCM structured data. Built monitoring dashboards using Evidently.ai or similar model observability tools. Why Join Us Be part of a domain-driven AI platform blending supply chain expertise with modern agentic LLM architectures. Work on real-world use cases that impact farm-to-plate food systems at scale. Collaborate with a fast-moving, cross-functional team that values innovation, ownership, and outcome. Learn and grow in a modular, full-stack AI environment where your ideas become deployed APIs. Enjoy flexibility in remote, experimentation, and continuous learning.

Job Description Management Level: Ind & Func AI Decision Science Analyst/Consultant Location: Bengaluru (Bangalore), Gurugram (Gurgaon), Hyderabad, Chennai. Must-have skills: Programming languages -Python/R, Generative AI, Large Language Models (LLMs), ML libraries such as Scikit-learn, TensorFlow, Torch, Lang Chain, or OpenAI API, RAG Applications. Good to have skills : Big data technologies such as Spark or Hadoop,AI model explainability(XAI),bias detection and AI ethics. Familiarity with Edge AI and deploying models on embedded devices for industrial automation. Experience with Reinforcement Learning (RL) and AI-driven optimization techniques. Job Summary We are looking for a Data Scientist / AI Specialist with 2-9years of experience to join our team and work on client projects in the Automotive & Industrial sectors. This role will involve leveraging traditional Machine Learning (ML), Generative AI (GenAI), Agentic AI, and Autonomous AI Systems to drive innovation, optimize processes, and enhance decision-making in complex industrial environments. Prior experience in the Auto/Industrial industry is a plus, but we welcome candidates from any domain with a strong analytical mindset and a passion for applying AI to real-world business challenges. Roles & Responsibilities: Develop, deploy and monitor AI/ML models in production environments & enterprise systems, including predictive analytics, anomaly detection, and process optimization for clients. Work with Generative AI models (e.g., GPT, Stable Diffusion, DALLE) for applications such as content generation, automated documentation, code synthesis, and intelligent assistants. Implement Agentic AI systems, including AI-powered automation, self-learning agents, and decision-support systems for industrial applications. Design and build Autonomous AI solutions for tasks like predictive maintenance, supply chain optimization, and robotic process automation (RPA). Work with structured and unstructured data from various sources, including IoT sensors, manufacturing logs, and customer interactions. Optimize and fine-tune LLMs (Large Language Models) for specific business applications, ensuring ethical and explainable AI use. Utilize MOps and AI orchestration tools to streamline model deployment, monitoring, and retraining cycles. Collaborate with cross-functional teams, including engineers, business analysts, and domain experts, to align AI solutions with business objectives. Stay updated with cutting-edge AI research in Generative AI, Autonomous AI, and Multi-Agent Systems. Professional & Technical Skills: 3-5 years of experience in Data Science, Machine Learning, or AI-related roles. Proficiency in Python (preferred) or R, and experience with ML libraries such as Scikit-learn, TensorFlow, Torch, Lang Chain, or OpenAI API. Strong understanding of Generative AI, Large Language Models (LLMs), and their practical applications. Hands-on experience in fine-tuning and deploying foundation models (e.g., OpenAI, Llama, Claude, Gemini, etc.). Experience with Vector Databases (e.g., FAISS, Chroma, Weaviate, Pinecone) for retrieval-augmented generation (RAG) applications. Knowledge of Autonomous AI Agents (e.g., AutoGPT, BabyAGI) and multi-agent orchestration frameworks. Experience working with SQL and NoSQL databases. Familiarity with cloud platforms (AWS, Azure, or GCP) for AI/ML model deployment. Strong problem-solving and analytical thinking abilities. Ability to communicate complex AI concepts to technical and non-technical stakeholders. Bonus: Experience in Automotive, Industrial, or Manufacturing AI applications (e.g., predictive maintenance, quality inspection, digital twins). Additional Information: Bachelor/Master’s degree in Statistics/Economics/ Mathematics/ Computer Science or related disciplines with an excellent academic record /MBA from top-tier universities. Excellent Communication and Interpersonal Skills. About Our Company | Accenture Job Qualifications Job Qualifications Experience: Minimum 2-9 years of relevant Data Science, Machine Learning or AI-related roles., Exposure to Industrial & Automotive Firms or Professional Services. Educational Qualification: Bachelor/Master’s degree in Statistics/Economics/ Mathematics/ Computer Science or related disciplines with an excellent academic record or MBA from top-tier universities.

Tech stack required: Programming languages: Python Public Cloud: AzureFrameworks: Vector Databases such as Milvus, Qdrant/ ChromaDB, or usage of CosmosDB or MongoDB as Vector stores. Knowledge of AI Orchestration, AI evaluation and Observability Tools. Knowledge of Guardrails strategy for LLM. Knowledge on Arize or any other ML/LLM observability tool. Experience: Experience in building functional platforms using ML, CV, LLM platforms. Experience in evaluating and monitoring AI platforms in production Nice to have requirements to the candidate Excellent communication skills, both written and verbal. Strong problem-solving and critical-thinking abilities. Effective leadership and mentoring skills. Ability to collaborate with cross-functional teams and stakeholders. Strong attention to detail and a commitment to delivering high-quality solutions. Adaptability and willingness to learn new technologies. Time management and organizational skills to handle multiple projects and priorities.

Lead AI Architect: We are seeking a highly skilled AI Architect to lead our team in developing cutting-edge generative AI solutions. This role requires a deep understanding of AI/ML concepts, strong technical expertise, and exceptional leadership abilities. As the Lead AI Architect, you will be the technical authority responsible for defining the strategy, architecture, and execution of cutting-edge generative AI initiatives. You will lead a talented team, driving the development of innovative solutions from concept through production deployment. This role demands a deep understanding of state-of-the-art AI models, advanced techniques like Retrieval-Augmented Generation (RAG) and fine-tuning, robust MLOps practices, and scalable cloud deployment strategies Role & responsibilities 1. Technical Vision & Architecture: a. Define and champion the technical vision and architectural roadmap for generative AI solutions (LLMs, Diffusion Models, Multimodal Models, etc.). b. Design, architect, and guide the development of sophisticated generative AI systems, incorporating techniques like fine-tuning, prompt engineering, and Reinforcement Learning from Human Feedback (RLHF). c. Architect and implement advanced Retrieval-Augmented Generation (RAG) pipelines, including vector database selection/optimization, chunking strategies, and multimodal RAG approaches. d. Ensure solutions are scalable, performant, cost-effective, reliable, and adhere to responsible AI principles (fairness, transparency, security). e. Stay relentlessly current with academic research and industry advancements in generative AI, MLOps, and related technologies, integrating relevant innovations. 2. MLOps & Lifecycle Management: a. Lead the design and implementation of robust MLOps pipelines for the entire generative AI lifecycle: data ingestion/preprocessing, model training/fine-tuning, validation, versioning (data, code, models), automated deployment, and continuous monitoring. Job Opening b. Establish best practices for model evaluation, performance tracking, drift detection, and ongoing optimization of generative models in production. c. Champion automation, reproducibility, and governance across all AI/ML workflows. 3. Deployment & Integration: a. Oversee the deployment of fine-tuned models and complex AI systems into scalable production environments using cloud platforms (AWS, GCP, Azure, potentially NVIDIA AI Enterprise). b. Design and implement containerized deployments (Docker, Kubernetes) and serverless architectures for AI model serving. c. Develop robust APIs (RESTful, gRPC) for seamless integration of AI capabilities into broader applications and services. d. Optimize models for inference speed and cost efficiency. 4. Leadership & Collaboration: a. Lead, mentor, and inspire a team of AI/ML engineers and data scientists. b. Collaborate closely with product managers, software engineers, data engineers, and business stakeholders to translate requirements into actionable AI strategies and technical designs. c. Effectively communicate complex technical concepts, architectural decisions, and project status to both technical and non-technical audiences, including executive leadership. d. Foster a culture of innovation, experimentation, and continuous improvement within the AI team Preferred candidate profile : • Typically 8-10+ years of hands-on experience in designing, building, and deploying production-grade AI/ML systems, with a significant recent focus (3+ years) on Generative AI. • Generative AI Expertise: Deep understanding and practical experience with foundational models (e.g., GPT series, Llama series, Stable Diffusion, multimodal models), transformer architectures, and key techniques like fine-tuning, prompt engineering, and RAG. • RAG Proficiency: Proven experience architecting and implementing RAG systems, including familiarity with vector databases (e.g., Pinecone, Weaviate, Milvus, ChromaDB) and embedding techniques. • MLOps Mastery: Demonstrable experience designing, building, and managing end-to-end MLOps pipelines using relevant tools and platforms (e.g., MLflow, Kubeflow, Vertex AI Pipelines, SageMaker Pipelines, Azure ML). • Programming & Frameworks: Expert-level proficiency in Python and core AI/ML libraries/frameworks (e.g., PyTorch, TensorFlow/Keras, JAX, Hugging Face ecosystem, LangChain, LlamaIndex). • Cloud Platforms: Strong hands-on experience with at least one major cloud provider (AWS, GCP, Azure) and their associated AI/ML services (e.g., SageMaker, Vertex AI, Azure Machine Learning). • Deployment Skills: Experience with containerization (Docker, Kubernetes), API development (REST, gRPC), and model serving/optimization techniques. • Leadership: Proven track record of technical leadership, mentoring teams, and successfully delivering complex AI projects from ideation to production. • Communication: Excellent verbal and written communication skills, with the ability to articulate complex technical ideas clearly and concisely. • Methodologies: Familiarity with Agile development practices and version control systems (Git). • Education: Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related quantitative field. (PhD preferred but not required). Preferred Qualifications : • Education: Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related quantitative field. (PhD preferred but not required). • Experience with multimodal generative models and RAG. • Experience with distributed training and large-scale model optimization (e.g., quantization, pruning). • Familiarity with data engineering tools and platforms (e.g., Spark, Kafka, Airflow). • Contributions to relevant open-source projects or publications in top AI/ML venues.

Skills: Excellent knowledge of Data Structures and Algorithms Great programming skills in Python Working knowledge C++ Experience in AI/ML, Data Science, CNN, RNN, LLM Classification, Clustering. Job Responsibilities: Designing and developing AI/ML algorithms to solve different technical problems in the domain of 3GPP. Explaining the designed data science algorithms clearly in document and presenting the same to support the innovation. Working in a team and share the responsibilities of integration, productisation and deployment.

Role & responsibilities As a Data Scientist specializing in Generative AI and NLP , you will be at the forefront of AI innovation. Your role will involve designing and deploying sophisticated models, including large language models (LLMs), to solve complex business problems. You will work closely with cross-functional teams to create scalable, data-driven solutions that bring AI-driven creativity and intelligence to life across various industries. Generative AI & NLP Development : Design, develop, and deploy advanced applications and solutions using Generative AI models (e.g., GPT, LLaMA, Mistral) and NLP algorithms to solve business challenges and unlock new opportunities for our clients. Model Customization & Fine-Tuning : Apply state-of-the-art techniques like LoRA , PEFT , and fine-tuning of large language models to adapt solutions to specific use cases, ensuring high relevance and impact. Innovative Problem Solving : Leverage advanced AI methodologies to tackle real-world business problems, providing creative and scalable AI-powered solutions that drive measurable results. Data-Driven Insights : Conduct deep analysis of large datasets, uncovering insights and trends that guide decision-making, improve operational efficiencies, and fuel innovation. Cross-Functional Collaboration : Work closely with Consulting, Engineering, and other teams to integrate AI solutions into broader business strategies, ensuring the seamless deployment of AI-powered applications. Client Engagement : Collaborate with clients to understand their unique business needs, provide tailored AI solutions, and educate them on the potential of Generative AI to drive business transformation. What do we expect? Generative AI & NLP Expertise : Extensive experience in developing and deploying Generative AI applications and NLP frameworks , with hands-on knowledge of LLM fine-tuning , model customization , and AI-powered automation . Hands-On Data Science Experience : 6+ years of experience in data science, with a proven ability to build and operationalize machine learning and NLP models in real-world environments. AI Innovation : Deep knowledge of the latest developments in Generative AI and NLP , with a passion for experimenting with cutting-edge research and incorporating it into practical solutions. Problem-Solving Mindset : Strong analytical skills and a solution-oriented approach to applying data science techniques to complex business problems. Communication Skills : Exceptional ability to translate technical AI concepts into business insights and recommendations for non-technical stakeholders.

Role: Gen AI Engineer Employment Type: FTE with Dimiour Work Location: Remote Shift Timings: General Day Shift IST Time Zone Experience Required: 5+ Years GenAI Engineers 5+ years of experts who are proficient in Python and familiar with AI/Gen AI frameworks. Experience with data manipulation libraries like Pandas and NumPy is crucial. Specific expertise in implementing and managing large language models (LLMs) is a must. Fast API experience for API development Should have hands on experience in Agentic AI Ability to diagnose accuracy drops in Retrieval-Augmented Generation (RAG) systems A solid grasp of software engineering principles, including version control (Git), continuous integration and continuous deployment (CI/CD) practices, and automated testing, is required. Experience in MLOps, ML engineering, and Data Science, with a proven track record of developing and maintaining AI solutions, is essential. Preferred candidate profile Immediate - 15days

Position Title: AI/ML Engineer Company: Cyfuture India Pvt. Ltd. Industry: IT Services and IT Consulting Location: Sector 81, NSEZ, Noida (5 Days Work From Office) Website: www.cyfuture.com About Cyfuture Cyfuture is a trusted name in IT services and cloud infrastructure, offering state-of-the-art data center solutions and managed services across platforms like AWS, Azure, and VMWare. We are expanding rapidly in system integration and managed services, building strong alliances with global OEMs like VMWare, AWS, Azure, HP, Dell, Lenovo, and Palo Alto. Position Overview We are hiring an experienced AI/ML Engineer to lead and shape our AI/ML initiatives. The ideal candidate will have hands-on experience in machine learning and artificial intelligence, with strong leadership capabilities and a passion for delivering production-ready solutions. This role involves end-to-end ownership of AI/ML projects, from strategy development to deployment and optimization of large-scale systems. Key Responsibilities Lead and mentor a high-performing AI/ML team. Design and execute AI/ML strategies aligned with business goals. Collaborate with product and engineering teams to identify impactful AI opportunities. Build, train, fine-tune, and deploy ML models in production environments. Manage operations of LLMs and other AI models using modern cloud and MLOps tools. Implement scalable and automated ML pipelines (e.g., with Kubeflow or MLRun). Handle containerization and orchestration using Docker and Kubernetes. Optimize GPU/TPU resources for training and inference tasks. Develop efficient RAG pipelines with low latency and high retrieval accuracy. Automate CI/CD workflows for continuous integration and delivery of ML systems. Key Skills & Expertise 1. Cloud Computing & Deployment Proficiency in AWS, Google Cloud, or Azure for scalable model deployment. Familiarity with cloud-native services like AWS SageMaker, Google Vertex AI, or Azure ML. Expertise in Docker and Kubernetes for containerized deployments Experience with Infrastructure as Code (IaC) using tools like Terraform or CloudFormation. 2. Machine Learning & Deep Learning Strong command of frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost. Experience with MLOps tools for integration, monitoring, and automation. Expertise in pre-trained models, transfer learning, and designing custom architectures. 3. Programming & Software Engineering Strong skills in Python (NumPy, Pandas, Matplotlib, SciPy) for ML development. Backend/API development with FastAPI , Flask , or Django . Database handling with SQL and NoSQL (PostgreSQL, MongoDB, BigQuery). Familiarity with CI/CD pipelines (GitHub Actions, Jenkins). 4. Scalable AI Systems Proven ability to build AI-driven applications at scale. Handle large datasets, high-throughput requests, and real-time inference. Knowledge of distributed computing: Apache Spark, Dask, Ray . 5. Model Monitoring & Optimization Hands-on with model compression, quantization, and pruning . A/B testing and performance tracking in production. Knowledge of model retraining pipelines for continuous learning. 6. Resource Optimization Efficient use of compute resources: GPUs, TPUs, CPUs . Experience with serverless architectures to reduce cost. Auto-scaling and load balancing for high-traffic systems. 7. Problem-Solving & Collaboration Translate complex ML models into user-friendly applications. Work effectively with data scientists, engineers, and product teams. Write clear technical documentation and architecture reports . Udisha Parashar Senior Talent Acquisition Specialist Mob: +91- 9301895707 Email: udisha.parashar@cyfuture.com URL: www.cyfuture.com

Role & responsibilities Develop and maintain AI-based solutions using Python. Design, develop, and deploy AI-driven applications with a focus on GenAI Chatbots & AI Agents. Implement GenAI Chatbots, including ingestion and query pipelines, using at least one cloud platform ( Azure, or GCP). Implement Agentic AI framework-based solutions. Develop data ingestion pipelines for efficient data processing and transformation. Ensure high data integrity and performance in AI-driven workflows. Deploy AI models on cloud platforms such as Azure, or GCP. Implement best practices for scalability, security, and cost optimization in cloud-based AI solutions. Conduct regular performance tuning, debugging, and troubleshooting of AI models and applications. Implement monitoring and logging mechanisms for AI-driven systems. Work closely with cross-functional teams, including data scientists, ML engineers, and software developers, to integrate AI solutions. Document technical specifications, workflows, and best practices for AI development. Stay updated with the latest advancements in GenAI, Agentic AI technologies to bring innovation into the organization. Preferred candidate profile Extensive experience in AI software development with strong proficiency in Python. Hands-on experience with GenAI, Agentic AI. Experience in developing Generative AI chatbots, Agentic AI frameworks including ingestion pipelines, query pipelines, and Retrieval-Augmented Generation (RAG) using at least one cloud platform. Solid understanding of machine learning techniques and algorithms. Strong knowledge of data structures, algorithms, and software design principles. Healthcare domain experience is a plus.

We are seeking a skilled and experienced Machine Learning Engineer specialized in Natural Language Processing (NLP) and Computer Vision to join our team. The ideal candidate should have a strong foundation in ML and AI principles, coupled with a proven track record of developing and deploying a minimum of 20+ models in these domains. Role & responsibilities Develop and implement state-of-the-art machine learning algorithms and models for NLP and computer vision applications. Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions. Conduct thorough data analysis, pre-processing, and feature engineering to extract meaningful insights from structured and unstructured data. Design and implement scalable and efficient machine learning pipelines for training, evaluation, and inference. Experiment with various deep learning architectures, hyperparameters, and optimization techniques to improve model performance. Stay updated with the latest advancements in ML and AI research and evaluate their potential applications in our domain. Work closely with software engineers to integrate ML models into production systems and ensure their robustness and scalability. Perform rigorous testing and validation of models to ensure their accuracy, reliability, and generalization capability. Continuously monitor and maintain deployed models, and implement necessary updates and enhancements as required. Document code, methodologies, and findings effectively, and contribute to internal knowledge sharing initiatives Preferred candidate profile Strong theoretical understanding of machine learning algorithms, deep learning frameworks, and statistical modeling techniques. Proficiency in programming languages such as Python, along with experience in popular ML libraries such as TensorFlow, PyTorch, or scikit-learn. Hands-on experience in developing and deploying machine learning models for NLP and computer vision tasks, with a demonstrable portfolio of projects. Solid understanding of natural language processing techniques, including text classification, sentiment analysis, named entity recognition, etc. Familiarity with computer vision concepts such as image classification, object detection, image segmentation, etc., and experience with frameworks like OpenCV or TensorFlow. Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes) is a plus.

Role & responsibilities Generative AI, NLP & MLE: Design, develop, deploy, and scale advanced applications using Generative AI models (e.g., GPT, LLaMA, Mistral), NLP techniques, and MLE/MLOps best practices to solve business challenges and unlock new opportunities. Model Customization & Fine-Tuning: Apply techniques such as LoRA, PEFT, and fine-tuning of LLMs to build domain-specific models aligned with business use cases, with a focus on making them deployable in real-world environments. ML Engineering & Deployment: Implement end-to-end ML pipelinesincluding data preprocessing, model training, versioning, testing, and deploymentusing tools like MLflow, Docker, Kubernetes, and CI/CD practices. Innovative Problem Solving: Leverage cutting-edge AI and ML methodologies to solve practical business problems and deliver measurable results. Scalable AI Solutions: Ensure robust deployment, monitoring, and retraining of models in production environments, working closely with data engineering and platform teams. Data-Driven Insights: Conduct deep analysis of structured and unstructured data to uncover trends, guide decisions, and optimize AI models. Cross-Functional Collaboration: Partner with Consulting, Engineering, and Platform teams to integrate AI/ML solutions into broader architectures and business strategies. Client Engagement: Work directly with clients to understand requirements, present tailored AI solutions, andprovide advice on the adoption and operationalization of Generative AI and ML. Preferred candidate profile Overall 5-9 years of experience with real-time experience in GenAI and MLE/MLOps. Expertise in Generative AI: Hands-on experience in designing and deploying LLM-based solutions with frameworks such as HuggingFace, LangChain, Transformers, etc. MLE & Production Readiness: Proven experience in building ML models that are scalable, reliable, and production-ready, including exposure to MLE/MLOps workflows and tools. Deployment Tools & Best Practices : Familiarity with containerization (Docker), orchestration (Kubernetes), model tracking (MLflow), and cloud platforms (AWS/GCP/Azure) for deploying AI solutions at scale. Proficiency in development using Python frameworks (such as Django/Flask) or other similar technologies. In-depth understanding of APIs, microservices architecture, and cloud-based deployment strategies. Innovation & Curiosity: A passion for staying updated with the latest in Gen AI, LLMs, and ML engineering practices. Communication : Ability to translate complex technical concepts into business-friendly insights and recommendations

Role & responsibilities Generative AI, NLP & MLE: Design, develop, deploy, and scale advanced applications using Generative AI models (e.g., GPT, LLaMA, Mistral), NLP techniques, and MLE/MLOps best practices to solve business challenges and unlock new opportunities. Model Customization & Fine-Tuning: Apply techniques such as LoRA, PEFT, and fine-tuning of LLMs to build domain-specific models aligned with business use cases, with a focus on making them deployable in real-world environments. ML Engineering & Deployment: Implement end-to-end ML pipelinesincluding data preprocessing, model training, versioning, testing, and deploymentusing tools like MLflow, Docker, Kubernetes, and CI/CD practices. Innovative Problem Solving: Leverage cutting-edge AI and ML methodologies to solve practical business problems and deliver measurable results. Scalable AI Solutions: Ensure robust deployment, monitoring, and retraining of models in production environments, working closely with data engineering and platform teams. Data-Driven Insights: Conduct deep analysis of structured and unstructured data to uncover trends, guide decisions, and optimize AI models. Cross-Functional Collaboration: Partner with Consulting, Engineering, and Platform teams to integrate AI/ML solutions into broader architectures and business strategies. Client Engagement: Work directly with clients to understand requirements, present tailored AI solutions, andprovide advice on the adoption and operationalization of Generative AI and ML. Preferred candidate profile Overall 5-9 years of experience with real-time experience in GenAI and MLE/MLOps. Expertise in Generative AI: Hands-on experience in designing and deploying LLM-based solutions with frameworks such as HuggingFace, LangChain, Transformers, etc. MLE & Production Readiness: Proven experience in building ML models that are scalable, reliable, and production-ready, including exposure to MLE/MLOps workflows and tools. Deployment Tools & Best Practices : Familiarity with containerization (Docker), orchestration (Kubernetes), model tracking (MLflow), and cloud platforms (AWS/GCP/Azure) for deploying AI solutions at scale. Proficiency in development using Python frameworks (such as Django/Flask) or other similar technologies. In-depth understanding of APIs, microservices architecture, and cloud-based deployment strategies. Innovation & Curiosity: A passion for staying updated with the latest in Gen AI, LLMs, and ML engineering practices. Communication : Ability to translate complex technical concepts into business-friendly insights and recommendations

Role & responsibilities Generative AI, NLP & MLE: Design, develop, deploy, and scale advanced applications using Generative AI models (e.g., GPT, LLaMA, Mistral), NLP techniques, and MLE/MLOps best practices to solve business challenges and unlock new opportunities. Model Customization & Fine-Tuning: Apply techniques such as LoRA, PEFT, and fine-tuning of LLMs to build domain-specific models aligned with business use cases, with a focus on making them deployable in real-world environments. ML Engineering & Deployment: Implement end-to-end ML pipelinesincluding data preprocessing, model training, versioning, testing, and deploymentusing tools like MLflow, Docker, Kubernetes, and CI/CD practices. Innovative Problem Solving: Leverage cutting-edge AI and ML methodologies to solve practical business problems and deliver measurable results. Scalable AI Solutions: Ensure robust deployment, monitoring, and retraining of models in production environments, working closely with data engineering and platform teams. Data-Driven Insights: Conduct deep analysis of structured and unstructured data to uncover trends, guide decisions, and optimize AI models. Cross-Functional Collaboration: Partner with Consulting, Engineering, and Platform teams to integrate AI/ML solutions into broader architectures and business strategies. Client Engagement: Work directly with clients to understand requirements, present tailored AI solutions, andprovide advice on the adoption and operationalization of Generative AI and ML. Preferred candidate profile Overall 5-9 years of experience with real-time experience in GenAI and MLE/MLOps. Expertise in Generative AI: Hands-on experience in designing and deploying LLM-based solutions with frameworks such as HuggingFace, LangChain, Transformers, etc. MLE & Production Readiness: Proven experience in building ML models that are scalable, reliable, and production-ready, including exposure to MLE/MLOps workflows and tools. Deployment Tools & Best Practices : Familiarity with containerization (Docker), orchestration (Kubernetes), model tracking (MLflow), and cloud platforms (AWS/GCP/Azure) for deploying AI solutions at scale. Proficiency in development using Python frameworks (such as Django/Flask) or other similar technologies. In-depth understanding of APIs, microservices architecture, and cloud-based deployment strategies. Innovation & Curiosity: A passion for staying updated with the latest in Gen AI, LLMs, and ML engineering practices. Communication : Ability to translate complex technical concepts into business-friendly insights and recommendations

Minimum experience: 5 years. Basic Guidelines : Ensure all submitted CVs are up to date. Maximum Notice Period 3 weeks Willingness to go for face 2 face interview in 24 hours. Employment verification 5 years and 7 years criminal background check. No fake and proxy interviews. Vendors will be blacklisted by TSI/ our Client, if guilty. Maintain the below tracker. Java Fullstack developer Primary skills: Java 8/11, Spring boot, Microservices, Rest API, UI REACT JS, REDUX Location: Bangalore and Chennai 2. Python + UI/API developer Primary skills: Python, Rest API, UI REACT JS, REDUX Location: Gurgaon 3.UI Engineer Primary skills: UI REACT JS, REDUX, JavaScript,ES6/Typescript,HTML5,CSS3,Bootstrap) Secondary skills: Java 8/11, Spring boot, Microservices, Rest API Location: Gurgaon and Bangalore 4. API Automation tester + Java scripting API Automation testing, Java scripting, SQL, MongoDB, Selenium Secondary skills: Rest assured / karate, Cucumber framework. DB Concepts, json queries Location: Bangalore and Gurgaon Job Description: 5+ years in Quality Assurance and testing. Demonstrated experience with Java or Python. Good experience in API automation using Selenium (Rest Assured or any other framework for automation and POSTMAN for manual). Preferably worked on creating frameworks. Must be well versed in BDD Cucumber frameworks. Working knowledge of MySQL and its implementation. Experience in creating scripts through REST APIs. Able to manage multiple work streams, independent, able to communicate well. Required Details Candidate's Full Name (As per passport): Contact Number: Email ID: Passport: LinkedIn ID link: Location (City & State): Relocation (Yes / No): Availability to join the project: Total working Experience: Currently on project (Yes/No): Have you Ever worked with Client: (If yes details) Any Interviews Pending: Any Offers in hand: Have you been submitted by Client: Any Issue with BGC: Bachelors details: Masters details: Rate: please submit your resumes to Naveen@tanishasystems.com

Role Overview: We are seeking a highly skilled and experienced Senior Data Scientist with a minimum of 4 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role. Responsibilities: Contribute to the design and implementation of state-of-the-art AI solutions. Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI. Collaborate with stakeholders to identify business opportunities and define AI project goals. Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges. Utilize generative AI techniques, such as LLMs, Agentic Framework to develop innovative solutions for enterprise industry use cases. Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities. Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment. Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs. Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs. Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly. Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency. Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases. Ensure compliance with data privacy, security, and ethical considerations in AI applications. Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. A Ph.D. is a plus. Minimum 4 years of experience in Data Science and Machine Learning. In-depth knowledge of machine learning, deep learning, and generative AI techniques. Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch. Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models. Familiarity with computer vision techniques for image recognition, object detection, or image generation. Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment. Expertise in data engineering, including data curation, cleaning, and preprocessing. Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems. Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models. Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions. Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels. Understanding of data privacy, security, and ethical considerations in AI applications. Track record of driving innovation and staying updated with the latest AI research and advancements. Good to Have Skills: Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models Utilize optimization tools and techniques, including MIP (Mixed Integer Programming. Deep knowledge of classical AIML (regression, classification, time series, clustering) Drive DevOps and MLOps practices, covering CI/CD and monitoring of AI models. Implement CI/CD pipelines for streamlined model deployment and scaling processes. Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines. Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation. Implement monitoring and logging tools to ensure AI model performance and reliability. Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment. Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models.

Role: AI Engineer Microsoft CoPilot Experience: 4 - 8 Years Location: Pune. Mandatory Skills: Microsoft Copilot, Gen AI, Azure AI, LLM, Langchain, RAG. Notice Period: Immediate Joiners Job Description: Requirements: Bachelors or Master’s degree in Computer Science, Data Science, or a related field. Proven experience in developing and deploying AI/ML models in real-world applications. Strong programming skills, especially in Python, and familiarity with version control systems like Git. Extensive experience in the Microsoft environment, with expertise in CoPilot and related technologies. Excellent problem-solving and communication skills. Ability to work independently and collaboratively in a fast-paced, dynamic environment. Responsibilities: Agent Development: Design, implement, and optimize AI agents using Microsoft CoPilot and related technologies. Develop custom AI solutions leveraging Power Automate, Azure OpenAI, and other Microsoft tools. Solution Integration: Deploy AI solutions within client environments, ensuring scalability and seamless integration with exiiting systems. Work with stakeholders to identify automation opportunities and tailor solutions to business needs. AI Algorithm and Model Implementation: Design and implement machine learning algorithms, focusing on natural language processing (NLP) and conversational AI. Perform data preprocessing, feature engineering, and model training to create high-performing solutions. Collaboration and Support: Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to deliver integrated solutions. Provide technical guidance and support to ensure the successful adoption and use of AI-driven tools. Continuous Improvement: Stay updated on advancements in AI, machine learning, and Microsoft’s AI technologies. Contribute to knowledge sharing by conducting training sessions and documenting best practices. Preferred Skills: Strong knowledge of Microsoft Power Automate, Azure AI, and CoPilot features. Proficiency in Python and its AI/ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Familiarity with building and managing cloud-based solutions, preferably on Microsoft Azure. Understanding of conversational AI technologies and chatbot frameworks. Experience with data analysis tools and techniques to uncover insights and optimize models.

Hiring AI & Data Retrieval Engineer with expertise in NLQ, Text-to-SQL, LLMs, LangChain, pgVector, PostgreSQL, vector search, Python, AI libraries, Agentic AI & API integration. Exp with NLP, RAG, BI tools, live projects & LLM fine-tuning preferred.

Preferred candidate profile 1. LLM Basics : (Llama, Gemini ) : Understand the basics of generative AI and LLMs, such as key terminology, uses, potential issues, and primary frameworks. One should know what the data is trained on and any potential biases/issues that there may be with the data . Knowledge on know exactly how big LLMs can be, how computationally expensive training will be, and the differences between training LLMs and machine learning models. 1. Prompt Engineering : Knowledge on designing inputs for LLMs once theyre developed. 2. Prompt Engineering with OpenAI : As a leading figure in LLMs and generative AI, it’s important to know how to use prompt engineering specifically with OpenAI tools, as you’ll likely be using them at some point in your career. 3. Question-Answering :Question-answering (QA) LLMs are a type of large language model that has been trained specifically to answer questions. 4. Fine-Tuning : Knowledge on Fine-tuning to improve the performance of an LLM on a variety of tasks, including text generation, translation, summarization, and question-answering. Customize LLMs for specific applications, such as customer service chatbots or medical diagnosis systems. Awareness on supervised learning. This involves providing the LLM with a dataset of labelled data, where each data point is a pair of input and output.. 1. Lang Chain : To architect complex LLM pipelines by chaining multiple models together (Classification, text generation, code generation, etc.)`Agents` to interact with all these external systems to execute actions dictated by LLMs. 2. Parameter Efficiency/Tuning : LORA 3. RAG Building : Generative AI, mastering RAG building—short for Retrieval-Augmented Generation—is becoming increasingly crucial 4. ML OPS and in particular LLMOps : Large Language Model Operations, is the practice of managing and maintaining large language models (LLMs) in a production setting 5. TensorFlow i s like a versatile toolbox for creating intelligent programs that can learn and understand various concepts, including machine learning, deep learning, and data science

Role & responsibilities We are looking for Gen AI Engineer position permanent position with US MNC for Bangalore/Chennai/Pune/Noida/Gurgaon location (Hybrid). Preferred candidate profile Experience Hands-on engineering role focused on designing, building, and deploying Generative AI and LLM-based solutions. The role requires deep technical proficiency in Python and modern LLM frameworks with the ability to contribute to roadmap development and cross-functional collaboration. Key Responsibilities: Design and develop GenAI/LLM-based systems using tools such as Langchain and Retrieval-Augmented Generation (RAG) pipelines. Implement prompt engineering techniques and agent-based frameworks to deliver intelligent, context-aware solutions. Collaborate with the engineering team to shape and drive the technical roadmap for LLM initiatives. Translate business needs into scalable, production-ready AI solutions. Work closely with business SMEs and data teams to ensure alignment of AI models with real-world use cases. Contribute to architecture discussions, code reviews, and performance optimization. Skills Required: Proficient in Python, Langchain, and SQL. Understanding of LLM internals, including prompt tuning, embeddings, vector databases, and agent workflows. Background in machine learning or software engineering with a focus on system-level thinking. Experience working with cloud platforms like AWS, Azure, or GCP. Ability to work independently while collaborating effectively across teams. Excellent communication and stakeholder management skills. Preferred Qualifications: Hands-on experience in LLMs and Generative AI techniques. Experience contributing to ML/AI product pipelines or end-to-end deployments. Familiarity with MLOps and scalable deployment patterns for AI models. Prior exposure to client-facing projects or cross-functional AI teams.

Role & responsibilities We are looking for Gen AI Engineer position permanent position with US MNC for Bangalore/Chennai/Pune/Noida/Gurgaon location (Hybrid). Preferred candidate profile Experience Hands-on engineering role focused on designing, building, and deploying Generative AI and LLM-based solutions. The role requires deep technical proficiency in Python and modern LLM frameworks with the ability to contribute to roadmap development and cross-functional collaboration. Key Responsibilities: Design and develop GenAI/LLM-based systems using tools such as Langchain and Retrieval-Augmented Generation (RAG) pipelines. Implement prompt engineering techniques and agent-based frameworks to deliver intelligent, context-aware solutions. Collaborate with the engineering team to shape and drive the technical roadmap for LLM initiatives. Translate business needs into scalable, production-ready AI solutions. Work closely with business SMEs and data teams to ensure alignment of AI models with real-world use cases. Contribute to architecture discussions, code reviews, and performance optimization. Skills Required: Proficient in Python, Langchain, and SQL. Understanding of LLM internals, including prompt tuning, embeddings, vector databases, and agent workflows. Background in machine learning or software engineering with a focus on system-level thinking. Experience working with cloud platforms like AWS, Azure, or GCP. Ability to work independently while collaborating effectively across teams. Excellent communication and stakeholder management skills. Preferred Qualifications: Hands-on experience in LLMs and Generative AI techniques. Experience contributing to ML/AI product pipelines or end-to-end deployments. Familiarity with MLOps and scalable deployment patterns for AI models. Prior exposure to client-facing projects or cross-functional AI teams.

Role & responsibilities We are looking for Gen AI Engineer position permanent position with US MNC for Bangalore/Chennai/Pune/Noida/Gurgaon location (Hybrid). Preferred candidate profile Experience Hands-on engineering role focused on designing, building, and deploying Generative AI and LLM-based solutions. The role requires deep technical proficiency in Python and modern LLM frameworks with the ability to contribute to roadmap development and cross-functional collaboration. Key Responsibilities: Design and develop GenAI/LLM-based systems using tools such as Langchain and Retrieval-Augmented Generation (RAG) pipelines. Implement prompt engineering techniques and agent-based frameworks to deliver intelligent, context-aware solutions. Collaborate with the engineering team to shape and drive the technical roadmap for LLM initiatives. Translate business needs into scalable, production-ready AI solutions. Work closely with business SMEs and data teams to ensure alignment of AI models with real-world use cases. Contribute to architecture discussions, code reviews, and performance optimization. Skills Required: Proficient in Python, Langchain, and SQL. Understanding of LLM internals, including prompt tuning, embeddings, vector databases, and agent workflows. Background in machine learning or software engineering with a focus on system-level thinking. Experience working with cloud platforms like AWS, Azure, or GCP. Ability to work independently while collaborating effectively across teams. Excellent communication and stakeholder management skills. Preferred Qualifications: Hands-on experience in LLMs and Generative AI techniques. Experience contributing to ML/AI product pipelines or end-to-end deployments. Familiarity with MLOps and scalable deployment patterns for AI models. Prior exposure to client-facing projects or cross-functional AI teams.