Area of interest in the desired candidate: E-commerce, Electronic products Job description Online customer advisory and sales leads and process management Client management Data analytics Identification of ideas for business growth Annual bonus
Key Responsibilities: Team Management: Sales & Target Achievement: Process Management: Client & Escalation Handling: Order Management: Data & Performance Analytics: Business Growth Initiatives: Reporting & Review:
Job profile Responsible for driving Sales for one of the business verticals by assisting customers in choosing the right product as per their needs. Education Qualification : Engineering Graduate / Management Graduate Industry: E-commerce and consumer durable products Area of interest in the desired candidate: E-commerce, Electronic products Job description Tele Sales Customer Advisory leads and process management Client management Identification of ideas for business growth Key Result Areas Ability to manage customer queries and provide the right product solutions Ability to analyse data and make business decisions Monitor customer journey and optimise for better conversions Interact with brands and get the right solutions for business building Key Skillset Good command of English and Hindi Quick learner innovative in approach and Tech-savvy Business acumen Ability to connect with consumers, understand their needs, and help them get the right solutions
Job Summary: We are looking for a highly skilled Senior Software Engineer to lead the development of scalable, high-performance web applications. You will work across the full stack, focusing on frontend with Next.js , backend APIs with Django REST Framework , and database design in PostgreSQL . The ideal candidate is also experienced in cloud deployment on AWS , and has strong proficiency in HTML, CSS, and JavaScript for crafting seamless UI/UX. Key Responsibilities: Design, develop, and maintain full-stack applications using Next.js and Django REST Framework . Architect and optimize PostgreSQL schemas and queries for performance and reliability. Deploy, monitor, and scale applications on AWS (EKS,EC2, S3, RDS, CloudWatch, etc.) . Build responsive, accessible, and performant frontend interfaces using HTML, CSS, JavaScript , and modern web standards. Collaborate with product managers, UI/UX designers, and other developers to define and deliver product features. Perform code reviews, write unit and integration tests, and enforce best practices in security and performance. Lead technical discussions and mentor junior developers. Must-Have Skills: 5+ years of hands-on experience in full-stack web development. Frontend : Expert in Next.js , React, HTML5, CSS3, JavaScript (ES6+). Backend : Deep experience with Django and Django REST Framework . Database : Strong in PostgreSQL , including query optimization and migrations. DevOps/Cloud : Proficient in AWS services (EKS, EC2, S3, RDS, IAM, CloudFront, etc.), Docker, CI/CD pipelines. Experience with RESTful APIs and modern authentication methods (JWT, OAuth). Familiar with Git workflows, Jira, and Agile development. Nice-to-Have Skills: Experience with serverless architecture (AWS Lambda, API Gateway). Familiarity with GraphQL, Redis, or Elasticsearch. Knowledge of accessibility (a11y) and web standards. Prior experience in a SaaS or D2C platform. Soft Skills: Strong problem-solving and debugging skills. Excellent written and verbal communication. Ability to work independently with minimal supervision. Leadership qualities and mentorship mindset. Education: Bachelors or Masters degree in Computer Science, Engineering, or related field (preferred but not mandatory with strong experience).
Senior AI Data Scientist (6+ years) Location: Mumbai • Work Type: Onsite • Team: Data & AI • Reporting to: CTO Company: Brandbuddiez About the Role Were looking for a hands-on Senior AI Data Scientist to lead endtoend development of ML/AI solutionsfrom problem framing and data strategy to model deployment and measurable business impact. Youll partner with engineering, product, and business stakeholders to ship reliable models in production and mentor a small team. What Youll Do Own the full ML lifecycle for 12 highimpact problem areas (discovery delivery iteration). Translate ambiguous business problems into testable hypotheses and modelable objectives. Design, train, evaluate, and ship models (incl. LLM/RAG, forecasting, ranking, NLP/CV as needed). Build robust offline/online evaluation (A/B tests, counterfactuals, guardrails, humanintheloop). Productionize models with softwareengineering rigor (versioning, CI/CD, monitoring, observability). Drive data quality: schema design, feature stores, labeling strategy, data contracts, governance. Communicate clearly with execs and nontechnical partners; influence roadmaps with data. Minimum Qualifications 6+ years in applied ML/Data Science with production impact (consumer or enterprise). Strong statistical foundations (causal inference, experimental design, uncertainty, metrics). Proficiency in Python and scientific stack (pandas, NumPy, scikitlearn); SQL fluency. Experience training and evaluating modern ML (treebased methods, GLMs, embeddings, deep learning). Experience deploying to production with at least one MLOps stack (e.g., MLflow, Kubeflow, SageMaker, Vertex, Databricks) and containerized services (Docker/Kubernetes). Track record of partnering with engineering and product; excellent written and verbal communication. Preferred (Nice to Have) LLMs and Retrieval Augmented Generation (RAG): prompt/retrieval design, evaluation, safety/guardrails. Generative AI: finetuning, adapters/LoRA, instruction tuning, synthetic data, vector DBs (FAISS/PGV/Chroma). Recsys/ranking, timeseries forecasting, or causal ML for growth/marketing experimentation. Data engineering exposure (dbt, Spark, Kafka) and production telemetry/monitoring (Prometheus, Grafana, Evidently). Experience with privacy, responsible AI, and model risk management. Core Tech & Tools (illustrative) Languages: Python, SQL. Libraries: scikitlearn, XGBoost/LightGBM, PyTorch/TensorFlow, Hugging Face, Ray. Data: Snowflake/BigQuery/Redshift, dbt, Airflow. MLOps: MLflow, Weights & Biases, Docker, Kubernetes, Feast/Featureform. Serving: FastAPI, gRPC, AWS/GCP/Azure serverless, vector DBs. Analytics/Experimentation: Amplitude/GA, Optimizely/Statsig, A/B testing frameworks.