409 Llm Jobs - Page 14

GEN AI EngineersRole & responsibilities Are you a driven problem solver looking to help build next generation AI solutions join Altimetrik's Generative AI engineering team. If you are looking for a rapidly growing, collaborative environment with opportunities to make an impact and grow, our GenAI engineering team would be a great fit for you! Proven expertise in Prompt Engineering. Strong knowledge of NLP techniques, including text summarization, question answering, and more. Proficient in software design patterns, with experience in writing modular, reusable, and maintainable code. Hands-on experience with at least one major cloud platform (AWS, GCP, or Azure). Relevant experience with Docker and Kubernetes is highly desirable. Strong ability to rapidly prototype ideas and tackle complex problems with innovative solutions. Experience working within an Agile development environment. Excellent communication and presentation skills. Preferred candidate profile A Bachelor's degree in a relevant field such as Computer Science, Software Engineering, or Data Science is preferred. A Masters degree in Artificial Intelligence is a plus. 3-8 years of experience working with Python and a high level of proficiency in the language. Outstanding analytical and problem-solving abilities. Extensive experience with Generative AI, with a solid understanding of end-to-end pipelines involving Large Language Models (LLMs) such as OpenAI, Llama, Claude, etc. Skill as Mandate : - GEN AI (Minimum 6 months + experience ) Python(Strong coding skill ) Pandas Numpy

Job Title: Senior AI Engineer Generative AI (Product Development) Location: Hyderabad (Remote) Duration: 6 Months (Possible Extension) Experience Required: Minimum 12 Years Role Overview We are seeking a highly experienced Senior AI Engineer with deep expertise in Generative AI and a strong foundation in building and deploying agentic frameworks . The ideal candidate should have extensive experience in cloud infrastructure, specifically with AWS and GCP , and proven skills in full-cycle product development. The role requires a track record of working in startup environments , where the ability to adapt and drive innovative solutions is key. Key Responsibilities Design, develop, and deploy Generative AI -based applications, focusing on building and optimizing agentic frameworks for enterprise-grade products. Build and optimize LLM pipelines and integrate them with various data sources and APIs to enable scalable AI solutions. Implement robust CI/CD pipelines and software engineering best practices to ensure the reliability and scalability of AI models in production environments. Collaborate with cross-functional teams including product managers , designers , data engineers , and DevOps to bring concepts to life in a dynamic, startup-driven culture. Integrate AI services with AWS and GCP cloud platforms, ensuring scalability, security, and performance for high-volume AI workloads. Work on API integrations and ensure seamless communication between AI services and other product components. Ensure the reliability , security , and performance of AI systems through monitoring, load balancing, and containerization. Lead efforts in building and deploying AI-based agentic solutions , aimed at solving complex real-world problems. Stay updated on the latest AI trends and deep learning frameworks to continuously enhance product capabilities. Required Technical Skills Strong software engineering foundation , with deep expertise in CI/CD , unit testing , integration testing , and design patterns . Proficiency in Python , especially for building and deploying AI models, and integrating with APIs and cloud services. Extensive experience in building and deploying agentic frameworks that utilize LLMs and transformers . Strong experience working with AWS and GCP , specifically in deploying and scaling AI-driven applications . Familiarity with Git and modern version control workflows. Experience working with load balancing , high-availability systems , and techniques for managing large-scale AI workloads in the cloud. Nice-to-Have Skills Experience developing and consuming REST APIs for AI services and data integration. Familiarity with Docker for containerization, local development, and testing. Strong understanding of cloud-based data storage solutions such as AWS S3 , GCP Cloud Storage , and data lakes. Experience with Cloud AI Services such as AWS SageMaker , GCP AI Platform , and TensorFlow for scalable model deployment. Exposure to data warehousing solutions like Google BigQuery or AWS Redshift for integrating AI outputs with data systems. Experience with AI model monitoring , performance tuning , and continuous improvement of deployed models. Strong security knowledge related to cloud infrastructure, specifically for handling sensitive data in AI applications. Role & responsibilities Preferred candidate profile

Job Title: Senior AI Engineer Generative AI (Product Development) Location: Hyderabad (Remote) Duration: 6 Months (Possible Extension) Experience Required: Minimum 12 Years Role Overview We are seeking a highly experienced Senior AI Engineer with deep expertise in Generative AI and a strong foundation in building and deploying agentic frameworks . The ideal candidate should have extensive experience in cloud infrastructure, specifically with AWS and GCP , and proven skills in full-cycle product development. The role requires a track record of working in startup environments , where the ability to adapt and drive innovative solutions is key. Key Responsibilities Design, develop, and deploy Generative AI -based applications, focusing on building and optimizing agentic frameworks for enterprise-grade products. Build and optimize LLM pipelines and integrate them with various data sources and APIs to enable scalable AI solutions. Implement robust CI/CD pipelines and software engineering best practices to ensure the reliability and scalability of AI models in production environments. Collaborate with cross-functional teams including product managers , designers , data engineers , and DevOps to bring concepts to life in a dynamic, startup-driven culture. Integrate AI services with AWS and GCP cloud platforms, ensuring scalability, security, and performance for high-volume AI workloads. Work on API integrations and ensure seamless communication between AI services and other product components. Ensure the reliability , security , and performance of AI systems through monitoring, load balancing, and containerization. Lead efforts in building and deploying AI-based agentic solutions , aimed at solving complex real-world problems. Stay updated on the latest AI trends and deep learning frameworks to continuously enhance product capabilities. Required Technical Skills Strong software engineering foundation , with deep expertise in CI/CD , unit testing , integration testing , and design patterns . Proficiency in Python , especially for building and deploying AI models, and integrating with APIs and cloud services. Extensive experience in building and deploying agentic frameworks that utilize LLMs and transformers . Strong experience working with AWS and GCP , specifically in deploying and scaling AI-driven applications . Familiarity with Git and modern version control workflows. Experience working with load balancing , high-availability systems , and techniques for managing large-scale AI workloads in the cloud. Nice-to-Have Skills Experience developing and consuming REST APIs for AI services and data integration. Familiarity with Docker for containerization, local development, and testing. Strong understanding of cloud-based data storage solutions such as AWS S3 , GCP Cloud Storage , and data lakes. Experience with Cloud AI Services such as AWS SageMaker , GCP AI Platform , and TensorFlow for scalable model deployment. Exposure to data warehousing solutions like Google BigQuery or AWS Redshift for integrating AI outputs with data systems. Experience with AI model monitoring , performance tuning , and continuous improvement of deployed models. Strong security knowledge related to cloud infrastructure, specifically for handling sensitive data in AI applications. Role & responsibilities Preferred candidate profile

Technical Architect - GenAI Job Responsibilities Excellent communication skills and customer management skills 12+ years of deep technical expertise in distributed transactional scale & digital architectures. Experience in Solution Architecture Experience in Java or .NET and related frameworks Experience in GenAI, ChatGPT, LLM, RAG architectures Experience in CoPilot etc Experience in Cloud solutions. Either AWS or Azure or GCP Experience in Microservices and cloud native architectures Role Purpose The purpose of the role is to create exceptional architectural solution design and thought leadership and enable delivery teams to provide exceptional client engagement and satisfaction. Develop architectural solutions for the new deals/ major change requests in existing deals Creates an enterprise-wide architecture that ensures systems are scalable, reliable, and manageable. Provide solutioning of RFPs received from clients and ensure overall design assurance Develop a direction to manage the portfolio of to-be-solutions including systems, shared infrastructure services, applications in order to better match business outcome objectives Analyse technology environment, enterprise specifics, client requirements to set a collaboration solution design framework/ architecture Provide technical leadership to the design, development and implementation of custom solutions through thoughtful use of modern technology Define and understand current state solutions and identify improvements, options & tradeoffs to define target state solutions Clearly articulate, document and sell architectural targets, recommendations and reusable patterns and accordingly propose investment roadmaps Evaluate and recommend solutions to integrate with overall technology ecosystem Works closely with various IT groups to transition tasks, ensure performance and manage issues through to resolution Perform detailed documentation (App view, multiple sections & views) of the architectural design and solution mentioning all the artefacts in detail Validate the solution/ prototype from technology, cost structure and customer differentiation point of view Identify problem areas and perform root cause analysis of architectural design and solutions and provide relevant solutions to the problem Collaborating with sales, program/project, consulting teams to reconcile solutions to architecture Tracks industry and application trends and relates these to planning current and future IT needs Provides technical and strategic input during the project planning phase in the form of technical architectural designs and recommendations Collaborates with all relevant parties in order to review the objectives and constraints of solutions and determine conformance with the Enterprise Architecture. Identifies implementation risks and potential impacts. Enable Delivery Teams by providing optimal delivery solutions/ frameworks Build and maintain relationships with executives, technical leaders, product owners, peer architects and other stakeholders to become a trusted advisor Develops and establishes relevant technical, business process and overall support metrics (KPI/SLA) to drive results Manages multiple projects and accurately reports the status of all major assignments while adhering to all project management standards Identify technical, process, structural risks and prepare a risk mitigation plan for all the projects Ensure quality assurance of all the architecture or design decisions and provides technical mitigation support to the delivery teams Recommend tools for reuse, automation for improved productivity and reduced cycle times Leads the development and maintenance of enterprise framework and related artefacts Develops trust and builds effective working relationships through respectful, collaborative engagement across individual product teams Ensures architecture principles and standards are consistently applied to all the projects Ensure optimal Client Engagement Support pre-sales team while presenting the entire solution design and its principles to the client Negotiate, manage and coordinate with the client teams to ensure all requirements are met and create an impact of solution proposed Demonstrate thought leadership with strong technical capability in front of the client to win the confidence and act as a trusted advisor Competency Building and Branding Ensure completion of necessary trainings and certifications Develop Proof of Concepts (POCs),case studies, demos etc. for new growth areas based on market and customer research Develop and present a point of view of Wipro on solution design and architect by writing white papers, blogs etc. Attain market referencability and recognition through highest analyst rankings, client testimonials and partner credits Be the voice of Wipros Thought Leadership by speaking in forums (internal and external) Mentor developers, designers and Junior architects in the project for their further career development and enhancement Contribute to the architecture practice by conducting selection interviews etc Team Management Resourcing Anticipating new talent requirements as per the market/ industry trends or client requirements Hire adequate and right resources for the team Talent Management Ensure adequate onboarding and training for the team members to enhance capability & effectiveness Build an internal talent pool and ensure their career progression within the organization Manage team attrition Drive diversity in leadership positions Performance Management Set goals for the team, conduct timely performance reviews and provide constructive feedback to own direct reports Ensure that the Performance Nxt is followed for the entire team Employee Satisfaction and Engagement Lead and drive engagement initiatives for the team Track team satisfaction scores and identify initiatives to build engagement within the team Stakeholder Interaction Stakeholder Type Stakeholder Identification Purpose of Interaction Internal Delivery teamsReview of architectural process deployment in engagements, escalation management, issue resolution Pre-sales teamFor solutioning and architectural design purposePractice teamsTo understand the different solution portfoliosHolmes engineering and roll outFor automation purposeTalent Transformation Team, Competency GroupPlan and support delivery of Technical Trainings, knowledge sharing HRBPFor hiring and managing resourcesFinanceRevenue / budgetsTalent acquisition teamRecruitment External Vendors/ PartnersFor strategic alignment and partnerships, training Industry forums Best practices, market intelligence, knowledge sharing Display Lists the competencies required to perform this role effectively Functional Competencies/ Skill Domain/Industry Knowledge - Awareness and knowledge of broad economic, demographic, technological and global trends within own ecosystem - Expert Market Intelligence - Deep specialized understanding of the ecosystem practice, overall market & competition and nuances of delivery in that domain - Master Systems Thinking - Understanding of the Wipro system (interrelatedness, interdependencies and boundaries) and perform problem solving in a complex environment - Expert Leveraging Technology - In-depth knowledge of and mastery over ecosystem technology that commands expert authority respect - Master Asset reusability - ability to re-use the assets to ensure scalability and optimization - Expert Technical knowledge - architecture design principles, framework and documentation - Expert Competency Levels Foundation Knowledgeable about the competency requirements. Demonstrates (in parts) frequently with minimal support and guidance. Competent Consistently demonstrates the full range of the competency without guidance. Extends the competency to difficult and unknown situations as well. Expert Applies the competency in all situations and is serves as a guide to others as well. Master Coaches others and builds organizational capability in the competency area. Serves as a key resource for that competency and is recognised within the entire organization. Behavioral Competencies Strategic perspective Technology Acumen Innovation Managing Complexity Client centricity Execution excellence Change agility Passion for results Nurturing people Executive presence Deliver No. Performance Parameter Measure 1. Support sales team to create wins % of proposals with Quality Index >7, timely support of the proposals, identifying opportunities/ leads to sell services within/ outside account (lead generation), no. of proposals led 2. Delivery support CSAT, delivery as per cost, quality and timelines, Identify and develop resuable components, utilization %, Recommend tools for reuse, automation for improved productivity and reduced cycle times 3. Capability development % trainings and certifications completed, increase in ACE certifications, thought leadership content developed (white papers, Wipro PoVs) 4. Team Management Team attrition %, Employee satisfaction score

Role & responsibilities As an AI lead specializing in Generative AI, and Machine Learning Engineering (MLE), you will be at the forefront of AI innovation. Your role will involve designing, deploying, and operationalizing sophisticated modelsincluding large language models (LLMs)—to solve complex business problems. You will work closely with cross-functional teams to create scalable, production-ready AI solutions that drive intelligent automation and creativity across industries. Your Key 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 pipelines—including data preprocessing, model training, versioning, testing, and deployment—using 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, and provide advisory on the adoption and operationalization of Generative AI and ML.

What is AI Engineer team responsible for? As a Senior AI Engineer, youll be a key member of the Data & AI team. This team is responsible for designing and delivering data engineering, analytics, and generative AI solutions that drive meaningful business impact. Were looking for a pragmatic, results-driven problem solver who thrives in a fast-paced environment and is passionate about building solutions at scale. The ideal candidate has a strong technical foundation, a collaborative mindset, and the ability to navigate complex challenges. You should be comfortable working in a fast-moving, startup-like environment within an established enterprise, and should bring strong skill sets to adapt new solutions fast. You will play a crucial role in integrating AI solutions in our existing digital solutions, optimizing our data infrastructure, and enabling insights through data #MID_SENIOR_LEVEL What is a Digital & AI/ML Lead Engineer (Senior AI Engineer) responsible for? Serve as a hands-on technical lead, driving project execution and delivery in our growing AI team based in the Hyderabad office. Collaborate closely with the U.S.-based team and cross-functional stakeholders to understand business needs and deliver scalable, AI-powered solutions. Design and build AI applications leveraging best smart solutions. Provide quick prototype and evaluation AI/ML solutions aligned with business objectives. Stay current with emerging trends in AI, and machine learning and help implement best practices within the team. Mentor and support junior engineers, fostering a culture of learning and technical excellence. Manage unstructured data and generate embeddings that can further be leveraged into AI products. What ideal qualifications, skills & experience would help someone to be successful? Bachelors or master’s degree in computer science, data science, engineering, or a related field from a premium institute. 7+ years of experience in engineering, software engineering, data science, or machine learning, including 3+ years in a technical leadership role. Strong understanding with data pipelines, Snowflake ecosystem and master data management. Proficiency in Python. Experience working with unstructured data, large language models (LLMs), embeddings, and building generative AI prototypes. Self-starter with a passion for learning new tools and technologies. Strong communication skills and a collaborative, ownership-driven mindset. Work Shift Timings - 2:00 PM - 11:00 PM IST

Manage the US clients' post judgment collections/recovery efforts which includes preparation of legal documents Filing litigation - initiating a US lawsuit to resolve a legal dispute as per US bar guidelines Required Candidate profile Should have knowledge of USA collections laws Knowledge of US Debt recovery process is preferred Familiarity with the US Collections rules & regulations such as Fair Debt Collection Practice

Role & responsibilities We are looking for Data Scientist position with US MNC company for permanent role, Remote. Preferred candidate profile Mandatory Skills : 5 Years + relevant experience in data science. Experience in RAG implementation pipeline. Experience with AI/ML Frameworks like TensorFlow, PyTorch, or similar. Proficiency in Programming Languages such as Python, SQL, and other relevant languages. Experience in the Financial Services Industry and an understanding of compliance standards. (Preferred) Experience with Master data management, Data Wrangling and ETL Processes. Experience with data and AI/ML Technologies: Such as NLP/NLU, Azure Cognitive Services, Azure Synapse Analytics, Azure data bricks and Azure ML service. Certification in Data Science or AI/ML.

Job Description Requirement Name / Title GenAI Developer Work Location Chennai/ Bangalore/Pune/Kolkata Shift General Shift Mandatory Skills Gen AI, LLM, RAG, Lang chain, Mistral,Llama, Vector DB, Azure/GCP/ Lambda, Python, Tensorflow, Pytorch Preferred Skills GPT-4, NumPy, Pandas, Keras, Databricks, Pinecone/Chroma/Weaviate, Scale/Labelbox, Job Description / Roles & Responsibilities (in Detail) We are looking for a good Python Developer with Knowledge of Machine learning and deep learning framework. • Take care of entire prompt life cycle like prompt design, prompt template creation, prompt tuning/optimization for various GenAI base models • Design and develop prompts suiting project needs • Stakeholder management across business and domains as required for the projects • Evaluating base models and benchmarking performance • Implement prompt guardrails to prevent attacks like prompt injection, jailbreaking and prompt leaking • Develop, deploy and maintain auto prompt solutions • Design and implement minimum design standards for every use case involving prompt engineering • You will be responsible for training the machine learning and deep learning model. • Writing reusable, testable, and efficient code using Python • Design and implementation of low-latency, high-availability, and performant applications • Implementation of security and data protection • Integration of data storage solutions and API Gateways • Production change deployment and related support If you're interested or know someone who fits this role, feel free to reach out. WhatsApp: 9871530393 Email: shweta.gupta@sspearhead.com Warm Regards SHWETA GUPTA || Senior Recruitment Specialist Spearhead Professional Services Call/ WhatsApp: 9871530393 LinkedIn Profile: shwetagupta1810

Role : Principal Data Scientist / LLM Specialist Responsibilities : 1. Strategic Leadership :a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities.b. Oversee the design, development, and implementation of data science solutions.c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy :a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks.b. Design and implement data preprocessing, cleaning, and augmentation techniques.c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development :a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains.c. Apply advanced data mining and machine learning techniques to extract valuable insights.d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis :a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance.b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection.c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills :- 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field.- Deep understanding of LLM architectures, algorithms, and techniques.- Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries- Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must.- Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch)- Experience with data labeling and annotation tools. - Certifications in cloud platforms or ML technologies can be a plus.- Proficient knowledge of cloud platforms (AWS, GCP, Azure).- Strong problem-solving and analytical skills.- Ability to work in team-oriented, collaborative environment. Data Scientist- Statistician- Data Engineer- Python- R- Supervised & Unsupervised learning- Machine Learning Algorithms- Deep Learning- Hugging Face- Natural Language Processing (NLP)- Computer Vision- TensorFlow- PyTorch- Scikit-learn- Transformers- LLM Frameworks- LLM Models- GPT -3, 4o- BERT- Llama

Role : Principal Data Scientist / LLM Specialist Responsibilities : 1. Strategic Leadership :a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities.b. Oversee the design, development, and implementation of data science solutions.c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy :a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks.b. Design and implement data preprocessing, cleaning, and augmentation techniques.c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development :a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains.c. Apply advanced data mining and machine learning techniques to extract valuable insights.d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis :a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance.b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection.c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills :- 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field.- Deep understanding of LLM architectures, algorithms, and techniques.- Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries- Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must. - Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch)- Experience with data labeling and annotation tools.- Certifications in cloud platforms or ML technologies can be a plus.- Proficient knowledge of cloud platforms (AWS, GCP, Azure). - Strong problem-solving and analytical skills.- Ability to work in team-oriented, collaborative environment. Data Scientist- Statistician- Data Engineer- Python- R- Supervised & Unsupervised learning- Machine Learning Algorithms- Deep Learning- Hugging Face- Natural Language Processing (NLP)- Computer Vision- TensorFlow- PyTorch- Scikit-learn- Transformers- LLM Frameworks- LLM Models- GPT -3, 4o- BERT- Llama

Job Description & Responsibilities Identify valuable data sources and automate collection processes Undertake preprocessing of structured and unstructured data Analyze large amounts of information to discover trends and patterns Build predictive models and machine-learning algorithms Combine models through ensemble modeling Present information using data visualization techniques Propose solutions and strategies to business challenges Collaborate with engineering and product development teams. Qualification, Skills & Experience Good hands-on experience in Advanced Python Programming Experiences in scalable Microservice Architecture and Development Good understanding of API Development, Messaging Queues and Asynchronous Task Management Basic DevOp skills and working with Docker Experience with LangChain, LangGraph, AutoGen or CrewAI are a big plus Ability to co-work in a small team with different locations Ability to learn and understand a new Python framework

Responsibilities : 1. Strategic Leadership : a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of data science solutions. c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy : a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks. b. Design and implement data preprocessing, cleaning, and augmentation techniques. c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development : a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains. c. Apply advanced data mining and machine learning techniques to extract valuable insights. d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis : a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance. b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection. c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills : - 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field. - Deep understanding of LLM architectures, algorithms, and techniques. - Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries - Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must. - Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch) - Experience with data labeling and annotation tools. - Certifications in cloud platforms or ML technologies can be a plus. - Proficient knowledge of cloud platforms (AWS, GCP, Azure). - Strong problem-solving and analytical skills. - Ability to work in team-oriented, collaborative environment. Keywords : - Data Scientist - Statistician - Data Engineer - Python - R - Supervised & Unsupervised learning - Machine Learning Algorithms - Deep Learning - Hugging Face - Natural Language Processing (NLP) - Computer Vision - TensorFlow - PyTorch - Scikit-learn - Transformers - LLM Frameworks - LLM Models - GPT -3, 4o - BERT - Llama

Role : Principal Data Scientist / LLM Specialist Responsibilities : 1. Strategic Leadership : a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of data science solutions. c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy : a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks. b. Design and implement data preprocessing, cleaning, and augmentation techniques. c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development : a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains. c. Apply advanced data mining and machine learning techniques to extract valuable insights. d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis : a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance. b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection. c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills : - 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field. - Deep understanding of LLM architectures, algorithms, and techniques. - Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries - Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must. - Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch) - Experience with data labeling and annotation tools. - Certifications in cloud platforms or ML technologies can be a plus. - Proficient knowledge of cloud platforms (AWS, GCP, Azure). - Strong problem-solving and analytical skills. - Ability to work in team-oriented, collaborative environment. Keywords : - Data Scientist - Statistician - Data Engineer - Python - R - Supervised & Unsupervised learning - Machine Learning Algorithms - Deep Learning - Hugging Face - Natural Language Processing (NLP) - Computer Vision - TensorFlow - PyTorch - Scikit-learn - Transformers - LLM Frameworks - LLM Models - GPT -3, 4o - BERT - Llama

About the Role We are looking for an experienced and visionary Generative AI Architect with 8-12 years of experience in AI/ML, including hands-on work with LLMs (Large Language Models) and Generative AI solutions . In this strategic technical leadership role, you will be responsible for designing and overseeing the development of advanced GenAI platforms and solutions that transform business operations and customer experiences. As the GenAI Architect, you will work closely with data scientists, ML engineers, product teams, and stakeholders to conceptualize, prototype, and scale generative AI use cases across the organization or client engagements. Key Responsibilities GenAI Solution Architecture & Design Lead the design and development of scalable GenAI solutions leveraging LLMs , diffusion models , and multimodal architectures . Architect end-to-end pipelines involving prompt engineering , vector databases , retrieval-augmented generation (RAG) , and LLM fine-tuning . Select and integrate foundational models (e.g., GPT, Claude, LLaMA, Mistral ) based on business needs and technical constraints. Technical Strategy & Leadership Define GenAI architecture blueprints, best practices, and reusable components for rapid development and experimentation. Guide teams on model evaluation, inference optimization , and cost-effective scaling strategies . Stay current on the rapidly evolving GenAI landscape and assess emerging tools, APIs, and frameworks. Collaboration & Delivery Work with product owners, business leaders, and data teams to identify high-impact GenAI use cases across domains like customer support, content generation, document understanding, and code generation. Support PoCs, pilots, and production deployments of GenAI models in secure, compliant environments. Collaborate with MLOps and cloud teams to enable continuous delivery, monitoring, and governance of GenAI systems. Required Qualifications & Experience Education: Bachelor's or Masters degree in Computer Science, Artificial Intelligence, Data Science, or related technical field . PhD is a plus. Experience: 12–15 years in AI/ML and software engineering, with 3+ years focused on Generative AI and LLM-based architectures . Core Skills Deep expertise in machine learning , natural language processing (NLP) , and deep learning architectures . Hands-on experience with LLMs, transformers, fine-tuning techniques (LoRA, PEFT) , and prompt engineering . Proficient in Python , with libraries/frameworks such as Hugging Face Transformers, LangChain, OpenAI API, PyTorch, TensorFlow . Experience with vector databases (e.g., Pinecone, FAISS, Weaviate) and RAG pipelines . Strong understanding of cloud-native AI architectures (AWS/GCP/Azure), containerization (Docker/Kubernetes), and API integration. Architectural & Leadership Skills Proven ability to design and deliver scalable, secure, and efficient GenAI systems . Strong communication skills for cross-functional collaboration and stakeholder engagement. Ability to mentor engineering teams and drive innovation across the AI/ML ecosystem. Nice-to-Have Experience with multimodal models (text + image/audio/video). Knowledge of AI governance, ethical AI, and compliance frameworks . Familiarity with MLOps practices for GenAI , including model versioning, drift detection, and performance monitoring.

We are seeking a proficient AI/ML Engineer responsible for testing, deploying, and hosting various Large Language Models (LLMs). The role involves monitoring deployed agentic services, ensuring their optimal performance, and staying abreast of the latest advancements in AI technologies. Key Responsibilities: LLM Deployment & Hosting Evaluate, test, and deploy diverse LLMs on cloud-based and on-premise infrastructures. Optimize model performance for scalability and efficiency. Implement secure API endpoints and model-serving pipelines. Strong CI/CD skills and knowledge of CI/CD tools like Jenkins, GitHub Actions, CircleCI etc. Agentic AI Services Deploy and maintain AI agents using frameworks such as CrewAI, Agnos(Phi Data), AutoGen etc. Integrate LLMs into business workflows and automation tools. Design, monitor, and enhance agentic services for real-world applications. Monitoring & Optimization Utilize advanced observability tools to monitor model performance, latency, and cost efficiency. Implement tools like AgentOps, OpenLIT, Langfuse, and Langtrace for comprehensive monitoring and debugging of LLM applications. Develop logging, tracing, and alerting systems for deployed models and AI agents. Conduct A/B testing and gather user feedback to refine AI behavior. Address model drift and retrain models as necessary. Best Practices & Research Stay updated with the latest advancements in AI, LLMs, and agentic systems. Implement best practices for prompt engineering, reinforcement learning from human feedback (RLHF), and fine-tuning methodologies. Optimize compute costs and infrastructure usage for AI applications. Collaborate with researchers and ML engineers to integrate state-of-the-art AI techniques. Strong knowledge on version control tools like GitHub Qualifications & Skills: Proficiency with LLM frameworks such as Hugging Face Transformers, OpenAI API, or Meta AI models. Strong programming skills in Python and experience with deep learning libraries (PyTorch, TensorFlow, JAX). Experience with cloud platforms (AWS, Azure, GCP) and model deployment tools (Docker, Kubernetes, FastAPI, Ray Serve). Familiarity with vector databases (FAISS, Pinecone, Weaviate) and retrieval-augmented generation (RAG) techniques. Hands-on experience with monitoring tools such as AgentOps, OpenLIT, Langfuse, and Langtrace. Understanding of prompt engineering, LLM fine-tuning, and agent-based automation. Excellent problem-solving skills and the ability to work in a dynamic AI research and deployment team. Preferred Qualifications: Experience in reinforcement learning, fine-tuning LLMs, or training custom models. Knowledge of security best practices for AI applications. Contributions to open-source AI/ML projects or research publications in the field.

We are seeking an experienced AI/ML Specialist with a strong background in Natural Language Processing (NLP), Large Language Models (LLM), and Text-to-Speech (TTS) technologies. The ideal candidate will have a proven track record of designing and implementing AI/ML solutions, developing NLP pipelines, fine-tuning large language models, and working with TTS systems. This role offers the opportunity to work on cutting-edge AI-driven projects that have a transformative impact across industries.

Role : Principal Data Scientist / LLM Specialist Responsibilities : Strategic Leadership :a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities.b. Oversee the design, development, and implementation of data science solutions.c. Foster a culture of innovation and continuous improvement within the data science team. Data Strategy :a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks.b. Design and implement data preprocessing, cleaning, and augmentation techniques.c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. Data-Driven ML Development :a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains.c. Apply advanced data mining and machine learning techniques to extract valuable insights.d. Evaluate ML performance using rigorous data-driven metrics. ML Data Visualization and Analysis :a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance.b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection.c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills :- 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field.- Deep understanding of LLM architectures, algorithms, and techniques.- Strong experience in Python, SQL. Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question answering is must. Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch) Experience with data labeling and annotation tools. Certifications in cloud platforms or ML technologies can be a plus. Proficient knowledge of cloud platforms (AWS, GCP, Azure). Strong problem solving and analytical skills. Ability to work in team oriented, collaborative environment.Keywords : Data Scientist Statistician Data Engineer Python R Supervised & Unsupervised learning Machine Learning Algorithms Deep Learning Hugging Face Natural Language Processing (NLP) Computer Vision TensorFlow PyTorch Scikit learn Transformers LLM Frameworks LLM Models GPT 3, 4o BERT Llama

Responsibilities : 1. Strategic Leadership : a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of data science solutions. c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy : a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks. b. Design and implement data preprocessing, cleaning, and augmentation techniques. c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development : a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains. c. Apply advanced data mining and machine learning techniques to extract valuable insights. d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis : a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance. b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection. c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills : - 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field. - Deep understanding of LLM architectures, algorithms, and techniques. - Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries - Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must. - Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch) - Experience with data labeling and annotation tools. - Certifications in cloud platforms or ML technologies can be a plus. - Proficient knowledge of cloud platforms (AWS, GCP, Azure). - Strong problem-solving and analytical skills. - Ability to work in team-oriented, collaborative environment. Keywords : - Data Scientist - Statistician - Data Engineer - Python - R - Supervised & Unsupervised learning - Machine Learning Algorithms - Deep Learning - Hugging Face - Natural Language Processing (NLP) - Computer Vision - TensorFlow - PyTorch - Scikit-learn - Transformers - LLM Frameworks - LLM Models - GPT -3, 4o - BERT - Llama

Responsibilities : 1. Strategic Leadership : a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of data science solutions. c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy : a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks. b. Design and implement data preprocessing, cleaning, and augmentation techniques. c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development : a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains. c. Apply advanced data mining and machine learning techniques to extract valuable insights. d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis : a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance. b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection. c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills : - 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field. - Deep understanding of LLM architectures, algorithms, and techniques. - Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries - Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must. - Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch) - Experience with data labeling and annotation tools. - Certifications in cloud platforms or ML technologies can be a plus. - Proficient knowledge of cloud platforms (AWS, GCP, Azure). - Strong problem-solving and analytical skills. - Ability to work in team-oriented, collaborative environment. Keywords : - Data Scientist - Statistician - Data Engineer - Python - R - Supervised & Unsupervised learning - Machine Learning Algorithms - Deep Learning - Hugging Face - Natural Language Processing (NLP) - Computer Vision - TensorFlow - PyTorch - Scikit-learn - Transformers - LLM Frameworks - LLM Models - GPT -3, 4o - BERT - Llama

Role : Principal Data Scientist / LLM Specialist Responsibilities : 1. Strategic Leadership : a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of data science solutions. c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy : a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks. b. Design and implement data preprocessing, cleaning, and augmentation techniques. c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development : a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains. c. Apply advanced data mining and machine learning techniques to extract valuable insights. d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis : a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance. b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection. c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills : - 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field. - Deep understanding of LLM architectures, algorithms, and techniques. - Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries - Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must. - Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch) - Experience with data labeling and annotation tools. - Certifications in cloud platforms or ML technologies can be a plus. - Proficient knowledge of cloud platforms (AWS, GCP, Azure). - Strong problem-solving and analytical skills. - Ability to work in team-oriented, collaborative environment. Keywords : - Data Scientist - Statistician - Data Engineer - Python - R - Supervised & Unsupervised learning - Machine Learning Algorithms - Deep Learning - Hugging Face - Natural Language Processing (NLP) - Computer Vision - TensorFlow - PyTorch - Scikit-learn - Transformers - LLM Frameworks - LLM Models - GPT -3, 4o - BERT - Llama

: Role : Principal Data Scientist / LLM Specialist Responsibilities : 1. Strategic Leadership : a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of data science solutions. c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy : a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks. b. Design and implement data preprocessing, cleaning, and augmentation techniques. c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development : a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains. c. Apply advanced data mining and machine learning techniques to extract valuable insights. d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis : a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance. b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection. c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills : - 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field. - Deep understanding of LLM architectures, algorithms, and techniques. - Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries - Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must. - Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch) - Experience with data labeling and annotation tools. - Certifications in cloud platforms or ML technologies can be a plus. - Proficient knowledge of cloud platforms (AWS, GCP, Azure). - Strong problem-solving and analytical skills. - Ability to work in team-oriented, collaborative environment. Keywords : - Data Scientist - Statistician - Data Engineer - Python - R - Supervised & Unsupervised learning - Machine Learning Algorithms - Deep Learning - Hugging Face - Natural Language Processing (NLP) - Computer Vision - TensorFlow - PyTorch - Scikit-learn - Transformers - LLM Frameworks - LLM Models - GPT -3, 4o - BERT - Llama

You'll be Responsible for: Design, develop and implement cutting-edge AI/ML solutions, including Large Language Models (LLMs) and Generative AI applications Lead projects end-to-end while mentoring team members in AI-ML, including traditional ML and emerging AI technologies Drive innovation in AI agent development and orchestration for automated decision-making systems Establish best practices for responsible AI development and deployment What you'd have: AI-ML Engineer or Data Scientist with 3 11 years of relevant experience Strong expertise in modern AI frameworks and tools: ML/DL frameworks (TensorFlow, PyTorch, Keras, Sklearn) LLM frameworks (LangChain, LlamaIndex, CrewAI, AutoGen) Vector databases (Pinecone, Weaviate, ChromaDB) – Good To Have Hands-on experience with: Generative AI and foundation models Prompt engineering and LLM fine-tuning AI agent development and orchestration RAG (Retrieval-Augmented Generation) systems Proven experience in data preparation, including: Advanced data preprocessing techniques Feature engineering Data quality assessment and improvement Proficiency in data exploration tools (Pandas, Numpy, Polars) Strong programming skills in Python; R is a plus Deep understanding of: Statistics and probability ML/DL algorithms AI governance and responsible AI practices Experience with: Large-scale data processing Model deployment and MLOps Cost optimization for AI systems Track record of 3-5 successful AI/ML projects in production Excellent communication skills and team leadership ability Continuous learning mindset for emerging AI technologies Skills: Statistics and Mathematics Classical Machine Learning Deep Learning and Neural Networks Generative AI and LLMs Agentic AI Development MLOps and Production Engineering Data Engineering Model Optimization and Tuning AI Governance and Ethics Team Leadership Why join us? Impactful Work: Drive innovation in AI technologies while safeguarding Tanla's assets, data, and reputation Growth Opportunities: Be part of a rapidly growing company in the telecom and CPaaS space, with clear paths for professional advancement Innovative Environment: Work with a world-class team on cutting-edge AI technologies in a challenging and collaborative environment Research Opportunities: Contribute to the advancement of GenAI and Agentic AI technologies Tanla is an equal opportunity employer. We champion diversity and are committed to creating an inclusive environment for all employees. www.tanla.com

The Role : The ideal candidate for this role will be an innovative self-starter. You will be an AI expert with experience in making architectural tradeoffs to transform AI performance for a variety of use cases. You will collaborate with internal and external development engineers (architecture, hardware, validation, software services). You will contribute to development, support device characterization and benchmarking efforts. Responsibilities : - Proficiency in Large Models & Deep Neural Networks. - Hands-on experience in working with large models & deep neural networks. - Expertise in LLMs with working knowledge of large language models (LLMs). - Extensive experience in System platform Architecture. - Experience in Development Preferable for memory/storage/ any embedded system. - In depth knowledge and extensive experience in dealing with Standardizations/Technical Papers/Patents. - Extensive experience with C/C++ and Python programming. - Develop and fine-tune LLMs (GPT, Llama, Mistral, Falcon, Claude, etc.) for domain-specific applications. - Implement RAG pipelines using LlamaIndex, LangGraph, and vector databases (FAISS, Pinecone, Weaviate, ChromaDB) to enhance response accuracy. - Build AI-powered chatbots and autonomous agents using LangGraph, CrewAI, LlamaIndex, and OpenAI APIs. - Optimize and deploy generative AI models for real-time inference using cloud platforms (AWS, GCP, Azure) and MLOps tools (Docker, Kubernetes, MLflow). - Fine-tune models using LoRA, QLoRA, PEFT, and RLHF to improve efficiency and personalization. - Develop AI-driven workflows for structured reasoning and decision-making using CrewAI and LangGraph. - Integrate multi-modal AI models (text, image, speech) into enterprise solutions. - Implement memory and retrieval strategies for LLM-based systems using vector search and caching techniques. - Ensure AI models follow ethical AI guidelines, bias mitigation, and security best practices. Requirement : - Tech in Computer Science, Electrical Engineering. - 8 to 10 Years of experience in relevant domain. - Strong analytical & abstract thinking ability as well as technical communication skills. - Able to work independently and perform in fast paced environment. - Ability to troubleshoot and debug complex issues. - Working Knowledge on Device Driver is desirable. - Prior experience in working with Agile/Scrum. Required Skills : - Languages & Frameworks : Proficiency in Python, PyTorch, TensorFlow, JAX.

Responsibilities : 1. Strategic Leadership : a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of data science solutions. c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy : a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks. b. Design and implement data preprocessing, cleaning, and augmentation techniques. c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development : a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains. c. Apply advanced data mining and machine learning techniques to extract valuable insights. d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis : a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance. b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection. c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills : - 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field. - Deep understanding of LLM architectures, algorithms, and techniques. - Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries - Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must. - Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch) - Experience with data labeling and annotation tools. - Certifications in cloud platforms or ML technologies can be a plus. - Proficient knowledge of cloud platforms (AWS, GCP, Azure). - Strong problem-solving and analytical skills. - Ability to work in team-oriented, collaborative environment. Keywords : - Data Scientist - Statistician - Data Engineer - Python - R - Supervised & Unsupervised learning - Machine Learning Algorithms - Deep Learning - Hugging Face - Natural Language Processing (NLP) - Computer Vision - TensorFlow - PyTorch - Scikit-learn - Transformers - LLM Frameworks - LLM Models - GPT -3, 4o - BERT - Llama.