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

Must have Gen Ai + Deployment+ Machine learning 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.

Role & responsibilities What You'll Do: Analyse and integrate clinical and multi-omics datasets (e.g., genomics, proteomics) to extract actionable insights. Develop and validate predictive models for biomedical research and biomarker discovery. Apply Design of Experiments (DOE) and statistical methods to support research objectives. Build interactive web applications using Streamlit or R Shiny for data visualization and exploration. Ensure reproducibility, code quality, and adherence to industrialized coding best practices using Git/GitHub. Collaborate with cross-functional teams including bioinformaticians, data engineers, and clinicians. Document analysis workflows, methodologies, and results in a clear and structured manner. Communicate findings through reports, dashboards, and presentations to technical and non-technical stakeholders. Technical Skills Programming & Tools: R (Tidyverse), Python (pandas, NumPy, scikit-learn) Streamlit or R Shiny for web application development Git/GitHub for version control and code management GCP What You Need: Data Science & Modeling: Exploratory Data Analysis (EDA), Statistical Modeling, Predictive Modeling Clinical data interpretation and Omics data integration Design of Experiments (DOE), Reproducibility & Repeatability Data visualization (ggplot2, matplotlib, seaborn, etc.) You are important to us, lets stay connected! Every individual comes with a different set of skills and qualities so even if you don’t tick all the boxes for the role today, we urge you to apply as there might be a suitable/unique role for you tomorrow.We are an equal-opportunity employer. Our diverse and inclusive culture and values guide us to listen, trust, respect, and encourage people to grow the way they desire. Note: The designation will be commensurate with expertise and experience. Compensation packages are among the best in the industry.Additional Benefits: Health insurance (self & family), virtual wellness platform, and knowledge communities.

Job Title : Associate Principal India Locations: Hyderabad Who we are Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow. Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence. We are a Great Place to Work-Certified (2022-24), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the ‘Best’ and ‘Fastest Growing’ analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine Curious about the role? What your typical day would look like? Your work is a combination of hands-on contribution to Loreum Ipsum, Loreum Ipsum, etc. More specifically, this will involve: Lead and contribute to developing sophisticated machine learning models, predictive analytics, and statistical analyses to solve complex business problems. Demonstrate proficiency in programming languages such as Python or R, with the ability to write clean, efficient, and maintainable code. Experience with relevant libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn) is essential. Use your robust problem-solving skills to develop data-driven solutions, analyse complex datasets, and derive actionable insights that lead to impactful outcomes. Work closely with clients to understand their business objectives, identify opportunities for analytics-driven solutions, and communicate findings clearly and promptly. Take ownership of end-to-end model development, from problem definition and data exploration to model training, validation, and deployment. Collaborate with cross-functional teams, including data engineers, software developers, and business stakeholders, to integrate analytics solutions into business processes. Leverage a profound understanding of mathematical and statistical principles to guide developing and validating advanced data science models. Stay abreast of industry trends, emerging technologies, and best practices in data science, bringing innovative ideas to the team and contributing to continuous improvement. Desired Skills and Experience: 8 -10 years of total DS and model development experience Mandatory : Minimum 4+ years of experience in the Banking and Financial services industry A passion for writing high-quality code (Python), and the code should be modular, scalable, and end-end project execution while planning an active hands-on role Having good problem-solving skills is essential, and it is equally important to have in-depth knowledge to solve complex problems effectively. Comprehensive knowledge of the regression and classification concepts and mathematical backend along with SQL Encourage collaboration with various stakeholders and take complete ownership of deliverables. Adept understanding of various data science approaches, machine learning algorithms, and statistical methods. Excellent communication skills with presentability, articulation, storytelling capability, and ability to manage complex client situations Effective mentoring of a team with expertise in industry/domain/functional areas You are important to us, let’s stay connected! Every individual comes with a different set of skills and qualities so even if you don’t tick all the boxes for the role today, we urge you to apply as there might be a suitable/unique role for you tomorrow. We are an equal opportunity employer. Our diverse and inclusive culture and values guide us to listen, trust, respect, and encourage people to grow the way they desire. Note: The designation will be commensurate with expertise and experience. Compensation packages are among the best in the industry. Additional Benefits: Health insurance (self & family), virtual wellness platform, and knowledge communities

MLE/Sr. MLE Chennai, Bangalore, Hyderabad Who we are Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow. Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence. We are a Great Place to Work-Certified (2022-25), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the Best and Fastest Growing analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine. Curious about the role? What your typical day would look like? We are looking for a Machine Learning Engineer/Sr MLE who will work on a broad range of cutting-edge data analytics and machine learning problems across a variety of industries. More specifically, you will Engage with clients to understand their business context. Translate business problems and technical constraints into technical requirements for the desired analytics solution. Collaborate with a team of data scientists and engineers to embed AI and analytics into the business decision processes. What do we expect? 6+ years of experience with at least 4+ years of relevant MLOps experience. Proficient in a structured Python (Mandate) Proficient in Azure Databricks Follows good software engineering practices and has an interest in building reliable and robust software. Good understanding of DS concepts and DS model lifecycle. Working knowledge of Linux or Unix environments ideally in a cloud environment. Working knowledge of Spark/ PySpark is desirable. Model deployment / model monitoring experience is desirable. CI/CD pipeline creation is good to have. Excellent written and verbal communication skills. B.Tech from Tier-1 college / M.S or M. Tech is preferred. You are important to us, lets stay connected! Every individual comes with a different set of skills and qualities so even if you dont tick all the boxes for the role today, we urge you to apply as there might be a suitable/unique role for you tomorrow. We are an equal-opportunity employer. Our diverse and inclusive culture and values guide us to listen, trust, respect, and encourage people to grow the way they desire. Note: The designation will be commensurate with expertise and experience. Compensation packages are among the best in the industry. Additional Benefits: Health insurance (self & family), virtual wellness platform, and knowledge communities.

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.

Azure Databricks Lead (Sr. Data Engineer) - Hyderabad Who we are Tiger Analytics is a global analytics consulting firm. With data and technology at the core of our solutions, we are solving some of the toughest problems out there. Our culture is modeled around expertise and mutual respect with a team first mindset. Working at Tiger, youll be at the heart of this AI revolution. Youll work with teams that push the boundaries of what-is-possible and build solutions that energize and inspire. We are headquartered in the Silicon Valley and have our delivery centers across the globe. Role Overview: We are seeking street-smart and technically strong Senior Data Engineers / Leads who can take ownership of designing and developing cutting-edge data and AI platforms using Azure-native technologies and Databricks. You will play a critical role in building scalable data pipelines, modern data architectures, and intelligent analytics solutions. Key Responsibilities: Design and implement scalable, metadata-driven frameworks for data ingestion, quality, and transformation across both batch and streaming datasets. Develop and optimize end-to-end data pipelines to process structured and unstructured data, enabling the creation of analytical data products. Build robust exception handling, logging, and monitoring mechanisms for better observability and operational support. Take ownership of complex modules and lead the development of critical data workflows and components. Provide guidance to data engineers and peers on best practices. Collaborate with cross-functional teams—including business consultants, data architects & scientists, and application developers—to deliver impactful analytics solutions. Required Qualifications: 5+ years of overall technical experience, with a minimum of 2 years of hands-on experience with Microsoft Azure and Databricks. Proven experience delivering at least one end-to-end Data Lakehouse solution on Azure Databricks using the Medallion Architecture. Strong working knowledge of the Databricks ecosystem , including: PySpark, Notebooks, Structured Streaming, Unity Catalog, Delta Live Tables, Workflows, and SQL Warehouse. Advanced programming , unit testing, and debugging skills in Python and SQL. Hands-on experience with Azure-native services such as: Azure Data Factory, ADLS Gen2, Azure SQL Database, and Event Hub. Solid understanding of data modeling techniques , including both Dimensional and Third Normal Form (3NF) models. Exposure to developing LLM/Generative AI -powered applications. Must have excellent understanding of CI/CD workflows using Azure DevOps. Bonus: Knowledge of Azure infrastructure, including provisioning, networking, security, and governance. Educational Background: Bachelor’s degree (B.E/B.Tech) in Computer Science, Information Technology, or a related field from a reputed institute (preferred). You are important to us, let’s stay connected! Every individual comes with a different set of skills and qualities so even if you don’t tick all the boxes for the role today we urge you to apply as there might be a suitable/unique role for you tomorrow. We are an equal- opportunity employer. Our diverse and inclusive culture and values guide us to listen, trust, respect, and encourage people to grow the way they desire, packages are among the best in industry. Note: The designation will be commensurate with expertise and experience. Compensation packages are among the best in the industry. Additional Benefits: Health insurance (self & family), virtual wellness platform, Car Lease Program and knowledge communities.

About Tiger Analytics Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow. Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence. We are a Great Place to Work-Certified™ (2022-24), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the ‘Best’ and ‘Fastest Growing’ analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine. About the role This role is for a highly analytical and detail-oriented individual to join our Credit Card Product team. The Credit Card Product Analyst will play a crucial role in leveraging data to understand customer behavior, product performance, and market trends, ultimately driving strategic product decisions and enhancing the overall credit card portfolio. Responsibilities: Data Extraction & Analysis: Extract, manipulate, and analyze large datasets using tools like SQL, SAS, Python, or R. Develop and maintain data models, dashboards, and reports to monitor key performance indicators (KPIs) related to credit card products (e.g., acquisition, activation, usage, retention, profitability, risk). Perform in-depth ad-hoc analysis to identify trends, opportunities, and areas for improvement across the credit card product lifecycle, P&L (revenue and cost components) Insight Generation & Storytelling: Translate complex data findings into clear, concise, and actionable insights and recommendations for product managers, senior leadership, and cross-functional teams. Develop compelling presentations and narratives to communicate insights effectively, articulating the "why" behind the data. Identify customer needs, behaviors, and pain points through data analysis to inform product enhancements and new feature development. Product Performance Monitoring & Optimization: Track and analyze the performance of existing credit card products, identifying drivers of success and areas of underperformance. Support the development and evaluation of business cases for new product initiatives, features, and pricing strategies. Collaborate with marketing, sales, risk, operations, and technology teams to implement data-driven product strategies and campaigns. Monitor competitive landscape and market trends to identify opportunities for product innovation and differentiation. Risk & Compliance Analytics: Work closely with the credit risk team to analyze credit card portfolio risk, identify potential issues, and support the development of risk mitigation strategies. Ensure all analytical activities comply with relevant regulatory requirements and internal policies. Process Improvement: Continuously seek opportunities to improve data collection, analysis, and reporting processes. Automate reporting and analytical tasks where possible to increase efficiency. Skill & Experience Experience: Proven experience (6+ years, depending on level) in a data analytics role, within the financial services industry, with a focus on credit cards. Hands-on experience with data extraction and manipulation using SQL, SAS, Python, or R. Experience with data visualization tools (e.g., Tableau, Power BI) is highly desirable. Familiarity with credit card product lifecycle, key metrics, and industry best practices. Skills: Strong Analytical & Problem-Solving Skills: Ability to interpret complex data, identify patterns, and draw meaningful conclusions. Excellent critical thinking and logical reasoning. Technical Proficiency: Advanced proficiency in SQL and/or SAS is essential. Experience with Python or R for data analysis is a strong plus. Data Visualization: Ability to create clear, compelling, and informative data visualizations. Business Acumen: Solid understanding of credit card business models, profitability drivers, and customer segments. Communication: Excellent written and verbal communication skills, with the ability to articulate complex analytical findings to both technical and non-technical audiences. Collaboration: Ability to work effectively in a cross-functional team environment. Proactive & Self-Motivated: A self-starter who can take initiative, manage multiple projects, and drive them to completion with minimal supervision. Attention to Detail: Meticulous approach to data accuracy and analysis. Good to have: Experience with big data technologies (e.g., Hadoop, Spark). Knowledge of advanced statistical modeling or machine learning techniques. Experience with A/B testing and experimental design. Familiarity with financial regulations pertaining to credit cards. Qualification Education: Bachelor's degree in a quantitative field such as Finance, Economics, Statistics, Mathematics, Computer Science, or a related discipline. A Master's degree is a plus. Looking for Tier 1 colleges / business schools. Show more Show less

AnAI innovator with 79 years of experiencein NLP and Machine Learning, who thrives on buildingproduction-grade GenAI solutionsthat matter.Youll architect and deploy LLM-powered applications, support MLOps initiatives, and mentor the next generation of AI talent. Experience inpharma or life sciencesis a plus! Key Responsibilities: Design, develop, and deploy NLP & Generative AI solutions, leveraging Large Language Models (LLMs), fine-tuning techniques, and AI-powered automation. Lead research and implementation of advanced NLP techniques, including transformers, embeddings, retrieval-augmented generation (RAG), and multi-modal models. Architect scalable NLP pipelines for text processing, entity recognition, summarization, question answering, and conversational AI. Develop and optimize LLM-powered chatbots, virtual assistants, and AI agents, ensuring efficiency, accuracy, and contextual awareness. Implement Agentic AI systems, enabling autonomous workflows powered by LLMs and task orchestration frameworks. Ensure LLM observability and guardrails, enhancing model monitoring, safety, fairness, and compliance in production environments. Optimize inference pipelines, leveraging quantization, model distillation, and retrieval-enhanced generation to improve performance and cost efficiency. Lead MLOps initiatives, including CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployments (AWS, GCP, Azure). Collaborate with cross-functional teams to integrate NLP & GenAI solutions into enterprise applications, ensuring robust API development and scalable microservices architecture. Mentor junior engineers, drive best practices in NLP/AI model development, and contribute to AI governance in regulated industries like pharma/life sciences. Key Qualifications: 7-9 years of experience in NLP, AI/ML, or data science, with a proven track record of delivering production-grade NLP & GenAI solutions. Deep expertise in LLMs, transformer architectures (BERT, GPT, T5, LLaMA, Mistral, etc.), and fine-tuning techniques. Strong knowledge of NLP pipelines, including text preprocessing, tokenization, embeddings, and named entity recognition (NER). Experience with retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, Chroma), and prompt engineering. Hands-on experience with Agentic AI systems, LLM observability tools, and AI safety guardrails. Proficiency in Python and backend development (Django/Flask preferred), with strong API and microservices expertise. Familiarity with MLOps, cloud platforms (AWS, GCP, Azure), and scalable model deployment strategies. Prior experience in life sciences, pharma, or other regulated industries is a plus. A problem-solving mindset with the ability to work independently, drive innovation, and mentor junior engineers.

Role & responsibilities Understand the Business Requirements and translate business requirements into conceptual, logical and physical Data models. Work as a principal advisor on data architecture, across various data requirements, aggregation data lake data models data warehouse etc. Lead cross-functional teams, define data strategies, andleveragethe latest technologies in data handling. Define and govern data architecture principles, standards, and best practices to ensure consistency, scalability, and security of data assets across projects. Suggest best modelling approach to the client based on their requirement and target architecture. Analyze and understand the Datasets and guide the team in creating Source to Target Mapping and Data Dictionaries, capturing all relevant details. Profile the Data sets to generate relevant insights. Optimize the Data Models and work with the Data Engineers to define the Ingestion logic, ingestion frequency and data consumption patterns. Establish data governance practices, including data quality, metadata management, and data lineage, to ensure data accuracy, reliability, and compliance. Drives automation in modeling activities Collaborate with Business Stakeholders, Data Owners, Business Analysts, Architects to design and develop next generation data platform. Closely monitor the Project progress and provide regular updates to the leadership teams on the milestones, impediments etc. Guide /mentor team members, and review artifacts. Contribute to the overall data strategy and roadmaps. Propose and execute technical assessments, proofs of concept to promote innovation in the data space. Preferred candidate profile Minimum 16 years of experience Deep understanding of data architecture principles, data modelling, data integration, data governance, and data management technologies. Experience in Data strategies and developing logical and physical data models on RDBMS, NoSQL, and Cloud native databases. Decent experience in one or more RDBMS systems (such as Oracle, DB2, SQL Server) Good understanding of Relational, Dimensional, Data Vault Modelling Experience in implementing 2 or more data models in a database with data security and access controls. Good experience in OLTP and OLAP systems Excellent Data Analysis skills with demonstrable knowledge on standard datasets and sources. Good Experience on one or more Cloud DW (e.g. Snowflake, Redshift, Synapse) Experience on one or more cloud platforms (e.g. AWS, Azure, GCP) Understanding of DevOps processes Hands-on experience in one or more Data Modelling Tools Good understanding of one or more ETL tool and data ingestion frameworks Understanding of Data Quality and Data Governance Good understanding of NoSQL Database and modeling techniques Good understanding of one or more Business Domains Understanding of Big Data ecosystem Understanding of Industry Data Models Hands-on experience in Python Experience in leading the large and complex teams Good understanding of agile methodology

Mandatory Skills- NLP,GenAI,Machine Learning,Deployment,LLM,MLops Design, develop, and deploy NLP & Generative AI solutions, leveraging Large Language Models (LLMs), fine-tuning techniques, and AI-powered automation. Lead research and implementation of advanced NLP techniques, including transformers, embeddings, retrieval-augmented generation (RAG), and multi-modal models. Architect scalable NLP pipelines for text processing, entity recognition, summarization, question answering, and conversational AI. Develop and optimize LLM-powered chatbots, virtual assistants, and AI agents, ensuring efficiency, accuracy, and contextual awareness. Implement Agentic AI systems, enabling autonomous workflows powered by LLMs and task orchestration frameworks. Ensure LLM observability and guardrails, enhancing model monitoring, safety, fairness, and compliance in production environments. Optimize inference pipelines, leveraging quantization, model distillation, and retrieval-enhanced generation to improve performance and cost efficiency. Lead MLOps initiatives, including CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployments (AWS, GCP, Azure). Collaborate with cross-functional teams to integrate NLP & GenAI solutions into enterprise applications, ensuring robust API development and scalable microservices architecture. Mentor junior engineers, drive best practices in NLP/AI model development, and contribute to AI governance in regulated industries like pharma/life sciences. Key Qualifications: 7-9 years of experience in NLP, AI/ML, or data science, with a proven track record of delivering production-grade NLP & GenAI solutions. Deep expertise in LLMs, transformer architectures (BERT, GPT, T5, LLaMA, Mistral, etc.), and fine-tuning techniques. Strong knowledge of NLP pipelines, including text preprocessing, tokenization, embeddings, and named entity recognition (NER). Experience with retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, Chroma), and prompt engineering. Hands-on experience with Agentic AI systems, LLM observability tools, and AI safety guardrails. Proficiency in Python and backend development (Django/Flask preferred), with strong API and microservices expertise. Familiarity with MLOps, cloud platforms (AWS, GCP, Azure), and scalable model deployment strategies. Prior experience in life sciences, pharma, or other regulated industries is a plus. A problem-solving mindset with the ability to work independently, drive innovation, and mentor junior engineers.

Role & responsibilities Understand the Business Requirements and translate business requirements into conceptual, logical and physical Data models. Work as a principal advisor on data architecture, across various data requirements, aggregation data lake data models data warehouse etc. Lead cross-functional teams, define data strategies, andleveragethe latest technologies in data handling. Define and govern data architecture principles, standards, and best practices to ensure consistency, scalability, and security of data assets across projects. Suggest best modelling approach to the client based on their requirement and target architecture. Analyze and understand the Datasets and guide the team in creating Source to Target Mapping and Data Dictionaries, capturing all relevant details. Profile the Data sets to generate relevant insights. Optimize the Data Models and work with the Data Engineers to define the Ingestion logic, ingestion frequency and data consumption patterns. Establish data governance practices, including data quality, metadata management, and data lineage, to ensure data accuracy, reliability, and compliance. Drives automation in modeling activities Collaborate with Business Stakeholders, Data Owners, Business Analysts, Architects to design and develop next generation data platform. Closely monitor the Project progress and provide regular updates to the leadership teams on the milestones, impediments etc. Guide /mentor team members, and review artifacts. Contribute to the overall data strategy and roadmaps. Propose and execute technical assessments, proofs of concept to promote innovation in the data space. Preferred candidate profile Minimum 16 years of experience Deep understanding of data architecture principles, data modelling, data integration, data governance, and data management technologies. Experience in Data strategies and developing logical and physical data models on RDBMS, NoSQL, and Cloud native databases. Decent experience in one or more RDBMS systems (such as Oracle, DB2, SQL Server) Good understanding of Relational, Dimensional, Data Vault Modelling Experience in implementing 2 or more data models in a database with data security and access controls. Good experience in OLTP and OLAP systems Excellent Data Analysis skills with demonstrable knowledge on standard datasets and sources. Good Experience on one or more Cloud DW (e.g. Snowflake, Redshift, Synapse) Experience on one or more cloud platforms (e.g. AWS, Azure, GCP) Understanding of DevOps processes Hands-on experience in one or more Data Modelling Tools Good understanding of one or more ETL tool and data ingestion frameworks Understanding of Data Quality and Data Governance Good understanding of NoSQL Database and modeling techniques Good understanding of one or more Business Domains Understanding of Big Data ecosystem Understanding of Industry Data Models Hands-on experience in Python Experience in leading the large and complex teams Good understanding of agile methodology

Mandatory Skills- NLP,GenAI,Machine Learning,Deployment,LLM,MLops Design, develop, and deploy NLP & Generative AI solutions, leveraging Large Language Models (LLMs), fine-tuning techniques, and AI-powered automation. Lead research and implementation of advanced NLP techniques, including transformers, embeddings, retrieval-augmented generation (RAG), and multi-modal models. Architect scalable NLP pipelines for text processing, entity recognition, summarization, question answering, and conversational AI. Develop and optimize LLM-powered chatbots, virtual assistants, and AI agents, ensuring efficiency, accuracy, and contextual awareness. Implement Agentic AI systems, enabling autonomous workflows powered by LLMs and task orchestration frameworks. Ensure LLM observability and guardrails, enhancing model monitoring, safety, fairness, and compliance in production environments. Optimize inference pipelines, leveraging quantization, model distillation, and retrieval-enhanced generation to improve performance and cost efficiency. Lead MLOps initiatives, including CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployments (AWS, GCP, Azure). Collaborate with cross-functional teams to integrate NLP & GenAI solutions into enterprise applications, ensuring robust API development and scalable microservices architecture. Mentor junior engineers, drive best practices in NLP/AI model development, and contribute to AI governance in regulated industries like pharma/life sciences. Key Qualifications: 7-9 years of experience in NLP, AI/ML, or data science, with a proven track record of delivering production-grade NLP & GenAI solutions. Deep expertise in LLMs, transformer architectures (BERT, GPT, T5, LLaMA, Mistral, etc.), and fine-tuning techniques. Strong knowledge of NLP pipelines, including text preprocessing, tokenization, embeddings, and named entity recognition (NER). Experience with retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, Chroma), and prompt engineering. Hands-on experience with Agentic AI systems, LLM observability tools, and AI safety guardrails. Proficiency in Python and backend development (Django/Flask preferred), with strong API and microservices expertise. Familiarity with MLOps, cloud platforms (AWS, GCP, Azure), and scalable model deployment strategies. Prior experience in life sciences, pharma, or other regulated industries is a plus. A problem-solving mindset with the ability to work independently, drive innovation, and mentor junior engineers.

We're looking for a talented and motivated React + Node.js Developer to join our dynamic team. If you're passionate about building responsive, scalable web applications and enjoy working in a collaborative, innovative environment, we'd love to hear from you. Key Responsibilities : Develop and maintain user-facing features using React.js Build reusable, efficient, and scalable front-end components and libraries Design and implement server-side logic using Node.js Collaborate with cross-functional teams to define, design, and ship new features Optimize applications for maximum speed and scalability Write unit and integration tests to ensure code quality Integrate third-party APIs and services Troubleshoot, debug, and upgrade existing systems Stay up to date with the latest industry trends and technologies Participate in code reviews to ensure best practices and high-quality code Contribute to the overall architecture and improvement of our tech stack Requirements : Proven experience as a React.js Developer (6+ years preferred) Strong experience with Node.js and Express.js Proficiency in JavaScript, HTML5, CSS3, and RESTful API development Familiarity with modern front-end build pipelines and tools (e.g., Webpack, Babel, NPM) Experience with state management tools like Redux or Context API Understanding of version control systems (Git) Knowledge of database systems (SQL or NoSQL) and experience with ORM tools (e.g., Sequelize, Mongoose) Ability to write clean, modular, and well-documented code Strong problem-solving skills and attention to detail Excellent communication and teamwork skills Experience with cloud platforms (AWS, Azure, etc.) is a plus Familiarity with Docker and containerization is a plus

We're looking for a talented and motivated React + Node.js Developer to join our dynamic team. If you're passionate about building responsive, scalable web applications and enjoy working in a collaborative, innovative environment, we'd love to hear from you. Key Responsibilities : Develop and maintain user-facing features using React.js Build reusable, efficient, and scalable front-end components and libraries Design and implement server-side logic using Node.js Collaborate with cross-functional teams to define, design, and ship new features Optimize applications for maximum speed and scalability Write unit and integration tests to ensure code quality Integrate third-party APIs and services Troubleshoot, debug, and upgrade existing systems Stay up to date with the latest industry trends and technologies Participate in code reviews to ensure best practices and high-quality code Contribute to the overall architecture and improvement of our tech stack Requirements : Proven experience as a React.js Developer (6+ years preferred) Strong experience with Node.js and Express.js Proficiency in JavaScript, HTML5, CSS3, and RESTful API development Familiarity with modern front-end build pipelines and tools (e.g., Webpack, Babel, NPM) Experience with state management tools like Redux or Context API Understanding of version control systems (Git) Knowledge of database systems (SQL or NoSQL) and experience with ORM tools (e.g., Sequelize, Mongoose) Ability to write clean, modular, and well-documented code Strong problem-solving skills and attention to detail Excellent communication and teamwork skills Experience with cloud platforms (AWS, Azure, etc.) is a plus Familiarity with Docker and containerization is a plus

AnAI innovator with 79 years of experiencein NLP and Machine Learning, who thrives on buildingproduction-grade GenAI solutionsthat matter.Youll architect and deploy LLM-powered applications, support MLOps initiatives, and mentor the next generation of AI talent. Experience inpharma or life sciencesis a plus! Key Responsibilities: Design, develop, and deploy NLP & Generative AI solutions, leveraging Large Language Models (LLMs), fine-tuning techniques, and AI-powered automation. Lead research and implementation of advanced NLP techniques, including transformers, embeddings, retrieval-augmented generation (RAG), and multi-modal models. Architect scalable NLP pipelines for text processing, entity recognition, summarization, question answering, and conversational AI. Develop and optimize LLM-powered chatbots, virtual assistants, and AI agents, ensuring efficiency, accuracy, and contextual awareness. Implement Agentic AI systems, enabling autonomous workflows powered by LLMs and task orchestration frameworks. Ensure LLM observability and guardrails, enhancing model monitoring, safety, fairness, and compliance in production environments. Optimize inference pipelines, leveraging quantization, model distillation, and retrieval-enhanced generation to improve performance and cost efficiency. Lead MLOps initiatives, including CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployments (AWS, GCP, Azure). Collaborate with cross-functional teams to integrate NLP & GenAI solutions into enterprise applications, ensuring robust API development and scalable microservices architecture. Mentor junior engineers, drive best practices in NLP/AI model development, and contribute to AI governance in regulated industries like pharma/life sciences. Key Qualifications: 7-9 years of experience in NLP, AI/ML, or data science, with a proven track record of delivering production-grade NLP & GenAI solutions. Deep expertise in LLMs, transformer architectures (BERT, GPT, T5, LLaMA, Mistral, etc.), and fine-tuning techniques. Strong knowledge of NLP pipelines, including text preprocessing, tokenization, embeddings, and named entity recognition (NER). Experience with retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, Chroma), and prompt engineering. Hands-on experience with Agentic AI systems, LLM observability tools, and AI safety guardrails. Proficiency in Python and backend development (Django/Flask preferred), with strong API and microservices expertise. Familiarity with MLOps, cloud platforms (AWS, GCP, Azure), and scalable model deployment strategies. Prior experience in life sciences, pharma, or other regulated industries is a plus. A problem-solving mindset with the ability to work independently, drive innovation, and mentor junior engineers.

Role & responsibilities Understand the Business Requirements and translate business requirements into conceptual, logical and physical Data models. Work as a principal advisor on data architecture, across various data requirements, aggregation data lake data models data warehouse etc. Lead cross-functional teams, define data strategies, andleveragethe latest technologies in data handling. Define and govern data architecture principles, standards, and best practices to ensure consistency, scalability, and security of data assets across projects. Suggest best modelling approach to the client based on their requirement and target architecture. Analyze and understand the Datasets and guide the team in creating Source to Target Mapping and Data Dictionaries, capturing all relevant details. Profile the Data sets to generate relevant insights. Optimize the Data Models and work with the Data Engineers to define the Ingestion logic, ingestion frequency and data consumption patterns. Establish data governance practices, including data quality, metadata management, and data lineage, to ensure data accuracy, reliability, and compliance. Drives automation in modeling activities Collaborate with Business Stakeholders, Data Owners, Business Analysts, Architects to design and develop next generation data platform. Closely monitor the Project progress and provide regular updates to the leadership teams on the milestones, impediments etc. Guide /mentor team members, and review artifacts. Contribute to the overall data strategy and roadmaps. Propose and execute technical assessments, proofs of concept to promote innovation in the data space. Preferred candidate profile Minimum 16 years of experience Deep understanding of data architecture principles, data modelling, data integration, data governance, and data management technologies. Experience in Data strategies and developing logical and physical data models on RDBMS, NoSQL, and Cloud native databases. Decent experience in one or more RDBMS systems (such as Oracle, DB2, SQL Server) Good understanding of Relational, Dimensional, Data Vault Modelling Experience in implementing 2 or more data models in a database with data security and access controls. Good experience in OLTP and OLAP systems Excellent Data Analysis skills with demonstrable knowledge on standard datasets and sources. Good Experience on one or more Cloud DW (e.g. Snowflake, Redshift, Synapse) Experience on one or more cloud platforms (e.g. AWS, Azure, GCP) Understanding of DevOps processes Hands-on experience in one or more Data Modelling Tools Good understanding of one or more ETL tool and data ingestion frameworks Understanding of Data Quality and Data Governance Good understanding of NoSQL Database and modeling techniques Good understanding of one or more Business Domains Understanding of Big Data ecosystem Understanding of Industry Data Models Hands-on experience in Python Experience in leading the large and complex teams Good understanding of agile methodology

Mandatory Skills- NLP,GenAI,Machine Learning,Deployment,LLM,MLops Design, develop, and deploy NLP & Generative AI solutions, leveraging Large Language Models (LLMs), fine-tuning techniques, and AI-powered automation. Lead research and implementation of advanced NLP techniques, including transformers, embeddings, retrieval-augmented generation (RAG), and multi-modal models. Architect scalable NLP pipelines for text processing, entity recognition, summarization, question answering, and conversational AI. Develop and optimize LLM-powered chatbots, virtual assistants, and AI agents, ensuring efficiency, accuracy, and contextual awareness. Implement Agentic AI systems, enabling autonomous workflows powered by LLMs and task orchestration frameworks. Ensure LLM observability and guardrails, enhancing model monitoring, safety, fairness, and compliance in production environments. Optimize inference pipelines, leveraging quantization, model distillation, and retrieval-enhanced generation to improve performance and cost efficiency. Lead MLOps initiatives, including CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployments (AWS, GCP, Azure). Collaborate with cross-functional teams to integrate NLP & GenAI solutions into enterprise applications, ensuring robust API development and scalable microservices architecture. Mentor junior engineers, drive best practices in NLP/AI model development, and contribute to AI governance in regulated industries like pharma/life sciences. Key Qualifications: 7-9 years of experience in NLP, AI/ML, or data science, with a proven track record of delivering production-grade NLP & GenAI solutions. Deep expertise in LLMs, transformer architectures (BERT, GPT, T5, LLaMA, Mistral, etc.), and fine-tuning techniques. Strong knowledge of NLP pipelines, including text preprocessing, tokenization, embeddings, and named entity recognition (NER). Experience with retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, Chroma), and prompt engineering. Hands-on experience with Agentic AI systems, LLM observability tools, and AI safety guardrails. Proficiency in Python and backend development (Django/Flask preferred), with strong API and microservices expertise. Familiarity with MLOps, cloud platforms (AWS, GCP, Azure), and scalable model deployment strategies. Prior experience in life sciences, pharma, or other regulated industries is a plus. A problem-solving mindset with the ability to work independently, drive innovation, and mentor junior engineers.

AnAI innovator with 79 years of experiencein NLP and Machine Learning, who thrives on buildingproduction-grade GenAI solutionsthat matter.Youll architect and deploy LLM-powered applications, support MLOps initiatives, and mentor the next generation of AI talent. Experience inpharma or life sciencesis a plus! Key Responsibilities: Design, develop, and deploy NLP & Generative AI solutions, leveraging Large Language Models (LLMs), fine-tuning techniques, and AI-powered automation. Lead research and implementation of advanced NLP techniques, including transformers, embeddings, retrieval-augmented generation (RAG), and multi-modal models. Architect scalable NLP pipelines for text processing, entity recognition, summarization, question answering, and conversational AI. Develop and optimize LLM-powered chatbots, virtual assistants, and AI agents, ensuring efficiency, accuracy, and contextual awareness. Implement Agentic AI systems, enabling autonomous workflows powered by LLMs and task orchestration frameworks. Ensure LLM observability and guardrails, enhancing model monitoring, safety, fairness, and compliance in production environments. Optimize inference pipelines, leveraging quantization, model distillation, and retrieval-enhanced generation to improve performance and cost efficiency. Lead MLOps initiatives, including CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployments (AWS, GCP, Azure). Collaborate with cross-functional teams to integrate NLP & GenAI solutions into enterprise applications, ensuring robust API development and scalable microservices architecture. Mentor junior engineers, drive best practices in NLP/AI model development, and contribute to AI governance in regulated industries like pharma/life sciences. Key Qualifications: 7-9 years of experience in NLP, AI/ML, or data science, with a proven track record of delivering production-grade NLP & GenAI solutions. Deep expertise in LLMs, transformer architectures (BERT, GPT, T5, LLaMA, Mistral, etc.), and fine-tuning techniques. Strong knowledge of NLP pipelines, including text preprocessing, tokenization, embeddings, and named entity recognition (NER). Experience with retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, Chroma), and prompt engineering. Hands-on experience with Agentic AI systems, LLM observability tools, and AI safety guardrails. Proficiency in Python and backend development (Django/Flask preferred), with strong API and microservices expertise. Familiarity with MLOps, cloud platforms (AWS, GCP, Azure), and scalable model deployment strategies. Prior experience in life sciences, pharma, or other regulated industries is a plus. A problem-solving mindset with the ability to work independently, drive innovation, and mentor junior engineers.

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 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, and provide advisory on the adoption and operationalization of Generative AI and ML.