As a Prompt Engineer Intern, you will work closely with our AI/ML team to develop and optimize prompts for natural language processing (NLP) models. You’ll help craft effective prompts that guide AI systems to generate precise, coherent, and relevant responses. This role will provide you hands-on experience in working with state-of-the-art language models, helping you grow your skills in prompt engineering and AI/ML concepts. Key Responsibilities: Collaborate with senior prompt engineers and AI researchers to design, test, and refine prompts for various NLP applications. Analyze the performance of prompts in different contexts and fine-tune them to improve model outputs. Assist in creating datasets for model training and validation. Conduct experiments with large language models to evaluate their capabilities and limitations. Write clear documentation for prompt engineering experiments and results. Research and stay updated on the latest advancements in NLP and AI technologies. Qualifications: Currently pursuing or recently completed a B.Tech. degree in Civil, Mechanical or Electrical Field. Strong analytical and problem-solving skills. Excellent written and verbal communication skills. Basic understanding of natural language processing (NLP) and machine learning (ML) concepts. Ability to work both independently and as part of a collaborative team. Show more Show less

We’re seeking a skilled Data Scientist with expertise in SQL, Python, AWS SageMaker , and Commercial Analytics to contribute to Team. You’ll design predictive models, uncover actionable insights, and deploy scalable solutions to recommend optimal customer interactions. This role is ideal for a problem-solver passionate about turning data into strategic value. Key Responsibilities Model Development: Build, validate, and deploy machine learning models (e.g., recommendation engines, propensity models) using Python and AWS SageMaker to drive next-best-action decisions. Data Pipeline Design: Develop efficient SQL queries and ETL pipelines to process large-scale commercial datasets (e.g., customer behavior, transactional data). Commercial Analytics: Analyze customer segmentation, lifetime value (CLV), and campaign performance to identify high-impact NBA opportunities. Cross-functional Collaboration: Partner with marketing, sales, and product teams to align models with business objectives and operational workflows. Cloud Integration: Optimize model deployment on AWS, ensuring scalability, monitoring, and performance tuning. Insight Communication: Translate technical outcomes into actionable recommendations for non-technical stakeholders through visualizations and presentations. Continuous Improvement: Stay updated on advancements in AI/ML, cloud technologies, and commercial analytics trends. Qualifications Education: Bachelor’s/Master’s in Data Science, Computer Science, Statistics, or a related field. Experience: 3-4 years in data science, with a focus on commercial/customer analytics (e.g., pharma, retail, healthcare, e-commerce, or B2B sectors). Technical Skills: Proficiency in SQL (complex queries, optimization) and Python (Pandas, NumPy, Scikit-learn). Hands-on experience with AWS SageMaker (model training, deployment) and cloud services (S3, Lambda, EC2). Familiarity with ML frameworks (XGBoost, TensorFlow/PyTorch) and A/B testing methodologies. Analytical Mindset: Strong problem-solving skills with the ability to derive insights from ambiguous data. Communication: Ability to articulate technical concepts to business stakeholders. Preferred Qualifications AWS Certified Machine Learning Specialty or similar certifications. Experience with big data tools (Spark, Redshift) or ML Ops practices. Knowledge of NLP, reinforcement learning, or real-time recommendation systems. Exposure to BI tools (Tableau, Power BI) for dashboarding. Show more Show less

We’re seeking a skilled Data Scientist with expertise in SQL, Python, AWS SageMaker , and Commercial Analytics to contribute to Team. You’ll design predictive models, uncover actionable insights, and deploy scalable solutions to recommend optimal customer interactions. This role is ideal for a problem-solver passionate about turning data into strategic value. Key Responsibilities Model Development: Build, validate, and deploy machine learning models (e.g., recommendation engines, propensity models) using Python and AWS SageMaker to drive next-best-action decisions. Data Pipeline Design: Develop efficient SQL queries and ETL pipelines to process large-scale commercial datasets (e.g., customer behavior, transactional data). Commercial Analytics: Analyze customer segmentation, lifetime value (CLV), and campaign performance to identify high-impact NBA opportunities. Cross-functional Collaboration: Partner with marketing, sales, and product teams to align models with business objectives and operational workflows. Cloud Integration: Optimize model deployment on AWS, ensuring scalability, monitoring, and performance tuning. Insight Communication: Translate technical outcomes into actionable recommendations for non-technical stakeholders through visualizations and presentations. Continuous Improvement: Stay updated on advancements in AI/ML, cloud technologies, and commercial analytics trends. Qualifications Education: Bachelor’s/Master’s in Data Science, Computer Science, Statistics, or a related field. Experience: 3-4 years in data science, with a focus on commercial/customer analytics (e.g., pharma, retail, healthcare, e-commerce, or B2B sectors). Technical Skills: Proficiency in SQL (complex queries, optimization) and Python (Pandas, NumPy, Scikit-learn). Hands-on experience with AWS SageMaker (model training, deployment) and cloud services (S3, Lambda, EC2). Familiarity with ML frameworks (XGBoost, TensorFlow/PyTorch) and A/B testing methodologies. Analytical Mindset: Strong problem-solving skills with the ability to derive insights from ambiguous data. Communication: Ability to articulate technical concepts to business stakeholders. Preferred Qualifications AWS Certified Machine Learning Specialty or similar certifications. Experience with big data tools (Spark, Redshift) or ML Ops practices. Knowledge of NLP, reinforcement learning, or real-time recommendation systems. Exposure to BI tools (Tableau, Power BI) for dashboarding. Show more Show less

Dimensionless Technologies is a AI product company that offers AI-based solutions to a diverse range of industries. Founded in 2016 in Mumbai, India, our journey has been marked by a relentless pursuit of excellence and a commitment to innovation. We are looking for a Smart, Intelligent and Hard working software Test Engineer. Responsibilities: Design, develop, and execute test plans and test cases based on software requirements. Perform manual testing to ensure software functionality and usability. Conduct performance testing to evaluate system responsiveness and stability under various conditions. Develop and maintain automation test scripts using Python to streamline testing processes. Utilize test management tools (e.g., JIRA, TestRail, or HP ALM) to organize, track, and report testing activities and results. Collaborate with developers and cross-functional teams in Agile environments, actively participating in daily stand-ups, sprint planning, and retrospectives. Identify, document, and report bugs, errors, and inconsistencies in software. Conduct testing in cloud-based environments, ensuring compatibility and performance on cloud platforms. Continuously evaluate and implement new testing tools, methodologies, and cloud-based solutions. Required Skills: Strong knowledge of manual testing techniques and tools. Expertise in performance testing tools like JMeter or LoadRunner. Proficiency in automation testing, preferred Python (e.g., Selenium, Pytest). Familiarity with test management tools like Azure board, JIRA, TestRail, or HP ALM for efficient test lifecycle management. Hands-on experience with cloud platforms like AWS, Azure, or Google Cloud for testing and deployments. Familiarity with Agile methodologies and software development life cycles (SDLC). Strong problem-solving and analytical abilities. Excellent communication and documentation skills. Preferred Qualifications: Bachelors degree in Computer Science, Engineering, or a related field. Experience with API testing and tools like Postman or RestAssured. Understanding of CI/CD pipelines and version control systems like Git. Knowledge of database testing and SQL queries.,

We’re seeking a skilled Data Scientist with expertise in SQL, Python, AWS SageMaker , and Commercial Analytics to contribute to Team. You’ll design predictive models, uncover actionable insights, and deploy scalable solutions to recommend optimal customer interactions. This role is ideal for a problem-solver passionate about turning data into strategic value. Key Responsibilities Model Development: Build, validate, and deploy machine learning models (e.g., recommendation engines, propensity models) using Python and AWS SageMaker to drive next-best-action decisions. Data Pipeline Design: Develop efficient SQL queries and ETL pipelines to process large-scale commercial datasets (e.g., customer behavior, transactional data). Commercial Analytics: Analyze customer segmentation, lifetime value (CLV), and campaign performance to identify high-impact NBA opportunities. Cross-functional Collaboration: Partner with marketing, sales, and product teams to align models with business objectives and operational workflows. Cloud Integration: Optimize model deployment on AWS, ensuring scalability, monitoring, and performance tuning. Insight Communication: Translate technical outcomes into actionable recommendations for non-technical stakeholders through visualizations and presentations. Continuous Improvement: Stay updated on advancements in AI/ML, cloud technologies, and commercial analytics trends. Qualifications Education: Bachelor’s/Master’s in Data Science, Computer Science, Statistics, or a related field. Experience: 3-4 years in data science, with a focus on commercial/customer analytics (e.g., pharma, retail, healthcare, e-commerce, or B2B sectors). Technical Skills: Proficiency in SQL (complex queries, optimization) and Python (Pandas, NumPy, Scikit-learn). Hands-on experience with AWS SageMaker (model training, deployment) and cloud services (S3, Lambda, EC2). Familiarity with ML frameworks (XGBoost, TensorFlow/PyTorch) and A/B testing methodologies. Analytical Mindset: Strong problem-solving skills with the ability to derive insights from ambiguous data. Communication: Ability to articulate technical concepts to business stakeholders. Preferred Qualifications AWS Certified Machine Learning Specialty or similar certifications. Experience with big data tools (Spark, Redshift) or ML Ops practices. Knowledge of NLP, reinforcement learning, or real-time recommendation systems. Exposure to BI tools (Tableau, Power BI) for dashboarding.