Delivery & Project Leadership • Translate business goals into sprint-level roadmaps, • Lead design/code reviews; enforce git-flow, CI/CD and documentation standards. • Chair weekly steering meetings; secure production acceptance and SLA sign-off.
Job Title: Business Acquisition Manager Location: Chennai, India Employment Type: Full-time | Immediate Requirement Reports : Chief Marketing Officer Travel: Moderate primarily within India Position Summary The company is looking for a dynamic, tech-savvy, results-oriented Business Acquisition Manager to connect enterprise challenges with transformative AI, ML, and Generative AI solutions. The ideal candidate combines technical fluency with strategic thinking, capable of decoding industry requirements, crafting tailored responses to complex RFPs/RFQs, and presenting impactful solutions to decision-makers. This role demands agility across technology domains and sharp business insight to identify and pursue growth opportunities across verticals. Key Responsibilities Analyze RFPs, RFQs, and other industry-issued documents to extract business and technical requirements Match client needs with relevant AI/ML and Generative AI solutions tailored to diverse sectors Identify and articulate use cases and applications of AI technologies for specific client challenges Present and explain technical solutions clearly to both technical and non-technical stakeholders Develop high quality collateralpresentations, proposals, solution briefsindependently or with a small team Conduct client meetings and deliver persuasive solution demonstrations at customer locations Collaborate with internal teams (product, marketing, technical) to ensure solution-market alignment Stay informed on emerging trends in AI/ML and Generative AI to proactively identify new business opportunities Leverage generative AI tools (e.g., ChatGPT, others) to efficiently develop documentation, decks, and responses Support the Chief Marketing Officer in strategic planning and execution of business acquisition initiatives Qualifications & Experience Bachelors degree in technology (B. Tech or equivalent) MBA or equivalent postgraduate degree from a recognized institution Minimum 5 years of relevant experience in business acquisition, preferably in AI/ML or software solution industries Proven track record of engaging enterprise clients and driving deal progression Technical Skills & Knowledge Strong understanding of AI, Machine Learning, and Generative AI concepts Familiarity with AI product applications across domains like manufacturing, BFSI, healthcare, etc. Experience analyzing and responding to RFPs/RFQs Proficiency in using AI-enabled productivity tools (e.g., ChatGPT, image/video generators, documentation assistants) Capability to translate technical capabilities into business value propositions Soft Skills Exceptional verbal and written communication skills Strong presentation and persuasion abilities Skilled in building relationships and networking with prospective clients Fast learner with the ability to grasp new technologies and tools quickly Collaborative mindset with leadership potential and initiative Equal Opportunity Statement The company is an equal opportunity employer. We value diversity and inclusion in the workplace and are committed to creating an environment where every team member can thrive.
Key Responsibilities Business Analysis, Rule Design & Stakeholder Management Engage with Risk, Compliance, and Business stakeholders to understand and translate fraud/risk detection objectives into rule-based logic. Translate model features and domain knowledge into precise, testable rules. Act as the central liaison between business and technical teams, facilitating workshops and signoffs. Rule Engine Life Cycle Management Define rule logic, thresholds, and parameters based on aggregates. Maintain detailed rule design documentation and ensure alignment with compliance/regulatory requirements. Validate rules against historical/synthetic datasets, ensuring accuracy, scalability, and performance. Monitor rule performance in UAT and production environments, tracking precision, recall, false positives/negatives, and drift. Validation & UAT Oversight Drive UAT execution cycles for fraud/risk rules using historical and synthetic datasets. Validate rules against KPIs (precision, recall, false positives/negatives, coverage). Ensure UAT reports and signoffs are completed in coordination with stakeholders. Project Management Lead cross-functional teams (business, data scientists, developers, QA, and DevOps) to ensure timely delivery of initiatives. Create and manage project plans, track deliverables, identify risks, and ensure alignment with milestones. Conduct Agile ceremonies (planning, daily standups, retrospectives) and manage stakeholder reporting. Domain Expertise Apply prior Banking & Financial Services domain knowledge, especially in fraud detection, risk management, payments, and transaction monitoring. Partner with model development teams to understand model features and enhance them with effective rules. Governance & Compliance Ensure compliance with regulatory frameworks (e.g., PCI-DSS, RBI, AML guidelines). Document rules, validations, and audit trails to support internal and external reviews. Required Skills & Experience Experience: 8 - 10 years of overall experience, at least 5+ years as Business Analyst in Banking Domain. Strong knowledge of rule engine lifecycle from rule design, validation, UAT, deployment, to monitoring. Proven track record in fraud/risk detection systems, compliance, or transaction monitoring platforms. Experience with aggregates, metrics, and performance validation in real-time systems. Hands-on exposure to UAT processes, test strategy (baseline, soak, stress, spike), and performance validation reporting. Excellent stakeholder management and communication skills (business, risk, compliance, technical). Strong leadership, decision-making, and problem-solving capabilities. Preferred Qualifications Bachelors/masters degree in business administration, Computer Science, Information Technology, or related field. Certifications: PMP, CBAP, CSM, or equivalent is an added advantage. Prior experience in Fraud Prevention, Credit Risk, or Transaction Monitoring systems. Knowledge of AI/ML model integration with rule-based systems an added advantage. Equal Opportunity Statement The company is an equal opportunity employer. We value diversity and inclusion in the workplace and are committed to creating an environment where every team member can thrive.
This role is responsible for managing the client expectations. Strategize with various stakeholders to meet customer requirements. KEY RESPONSIBILITIES Data Science: Develop machine learning models to support recommendation systems and NLP projects; provide actionable insights for product and service optimization. Data Engineering: Build and maintain scalable ETL pipelines, optimize data storage solutions (data lakes, columnar formats), and ensure data accuracy for analytics. Data Analysis and Insight Generation: Skilled in analyzing complex datasets to uncover trends and patterns; generate and present insights that drive strategic decisions and enhance client services. Stakeholder Collaboration: Work with product and service teams to understand data needs and translate them into technical solutions. Skills/ Competencies Required Technical Skills Proficiency with Python (Pandas, NumPy), SQL, and Java. Experience with LLMs, Lang Chain, and Generative AI technologies. Familiarity with ML frameworks (TensorFlow, PyTorch) and data engineering tools (Spark, Kafka). Microservices, CI CD, ML Strong data analysis skills and ability to present findings to both technical and non-technical stakeholders. Proficient understanding of key data engineering concepts, such as data lakes, columnar formats, ETL tools, and BI tools. Knowledge in Machine Learning, NLP, Recommender systems, personalization, Segmentation, microservices architecture and API development. Ability to adapt to a fast-paced, dynamic work environment and learn new technologies quickly. Soft Skills Work in a team/ Independently. Excellent Written & Verbal Communication Skills Solid critical thinking and questioning skills. High degree of flexibility - willing to fill in the gaps rather than relying on others Strong communication skills, especially in presenting data insights. Flexibility, problem-solving, and a proactive approach in a fast-paced environment Required Educational Qualification & Relevant Experience Bachelors Degree in Computer Science, Data Analytics, Engineering, or a related field. Minimum of 5 to 10 years of experience in data science and data engineering. Strong critical thinking abilities and the capacity to work autonomously. Proficient understanding of key data engineering concepts, such as data lakes, columnar formats, ETL tools, and BI tools High motivation, good work ethic, maturity and personal initiative. Strong oral and written communication skills. Quadratyx is an equal opportunity employer - we will never differentiate candidates on the basis of religion, caste, gender, language, disabilities or ethnic group. Quadratyx reserves the right to place/move any candidate to any company location, partner location or customer location globally, in the best interest of Quadratyx business.
Purpose of the Job/ Role : Own the end-to-end delivery of analytics & ML solutionsfrom scoping through production uptimeacting as the single point of accountability for client value realization while mentoring a high-caliber technical team. KEY RESPONSIBILITIES Delivery & Project Leadership: Translate business goals into sprint-level roadmaps, Lead design/code reviews; enforce git-flow, CI/CD and documentation standards. Chair weekly steering meetings; secure production acceptance and SLA sign-off. Data Science & Engineering: Build ML, NLP and recommender models; oversee feature pipelines and data-quality gates. Implement full MLOps lifecycle (Docker, Kubernetes, MLflow/Kubeflow); set up drift & latency monitoring. Design scalable ETL on AWS / Azure / GCP. Team Leadership & Mentoring: Coach and grow a 4-8-member DS/DE squad via code reviews, pair programming and brown-bag sessions. Drive hiring, onboarding and performance reviews; foster a culture of experimentation + production discipline. Stakeholder & Client Engagement: Act as primary contact for client data-science initiatives, present insights to exec & non-tech audiences. Support pre-sales with PoCs, effort estimates and Statements of Work. Technical Skills Proficiency with Python (Pandas, NumPy), SQL, and Java. Experience with LLMs, Lang Chain, and Generative AI technologies. Familiarity with ML frameworks (TensorFlow, PyTorch) and data engineering tools (Spark, Kafka). Strong data analysis skills and ability to present findings to both technical and non-technical stakeholders. Proficient understanding of key data engineering concepts, such as data lakes, columnar formats, ETL tools, and BI tools. Knowledge in Machine Learning, NLP, Recommender systems, personalization, Segmentation, microservices architecture and API development. Ability to adapt to a fast-paced, dynamic work environment and learn new technologies quickly. Soft Skills Work in a team/ Independently. Excellent Written & Verbal Communication Skills Solid critical thinking and questioning skills. High degree of flexibility - willing to fill in the gaps rather than relying on others Strong communication skills, especially in presenting data insights. Flexibility, problem-solving, and a proactive approach in a fast-paced environment Required Educational Qualification & Relevant Experience Bachelors Degree in Computer Science, Data Analytics, Engineering, or a related field from a Tier 1 institute or a Ph.D. in a relevant discipline. Corporate experience is mandatory. Minimum of 8 to 10 years of experience in data science and data engineering. Strong critical thinking abilities and the capacity to work autonomously. Proficient understanding of key data engineering concepts, such as data lakes, columnar formats, ETL tools, and BI tools High motivation, good work ethic, maturity and personal initiative. Strong oral and written communication skills. Quadratyx is an equal opportunity employer - we will never differentiate candidates on the basis of religion, caste, gender, language, disabilities or ethnic group. Quadratyx reserves the right to place/move any candidate to any company location, partner location or customer location globally, in the best interest of Quadratyx business.
Role We are seeking a highly skilled and experienced Senior Data Science & ML Engineer to join our innovative AI team. This role is perfect for a hands-on expert with 4-5 years of experience who is passionate about building intelligent, production-ready AI systems, with a particular emphasis on Generative AI, Large Language Models (LLMs), and Agentic AI. You will be instrumental in designing, developing, and deploying advanced machine learning solutions across various domains, including Computer Vision and Natural Language Processing. You'll work with the latest tools and techniques to bring state-of-the-art research into practical, scalable applications. Technical Skills Experience with cloud platforms (AWS, Azure, GCP) and their respective AI/ML services (e.g., SageMaker, Azure ML, Vertex AI). Familiarity with MLOps tools and practices beyond Docker (e.g., Kubernetes, MLflow, CI/CD pipelines). Contributions to open-source projects or relevant publications in AI/ML conferences. Knowledge of vector databases (e.g., Pinecone, Weaviate, Milvus). Strong communication and collaboration skills, with the ability to explain complex technical concepts to non-technical stakeholders. Soft Skills Work in a team/ Independently. Excellent Written & Verbal Communication Skills Solid critical thinking and questioning skills. High degree of flexibility - willing to fill in the gaps rather than relying on others Strong communication skills, especially in presenting data insights. Flexibility, problem-solving, and a proactive approach in a fast-paced environment Required Educational Qualification & Relevant Experience Experience: 4-5 years of hands-on experience in Data Science, Machine Learning Engineering, or a related field, with a strong portfolio of deployed solutions. Programming: Python Generative AI & LLMs: Generative AI, LLMs AI Orchestration: LangChain, LangGraph, Agentic AI LLM Techniques: RAG (Retrieval Augmented Generation), Prompt Engineering, LLM Finetuning ML Domains: Natural Language Processing (NLP), Computer Vision ML Lifecycle: End-to-end ML Model Lifecycle management API Development: Flask, FastAPI Containerization: Docker Rapid Prototyping: Streamlit Problem-Solving: Excellent analytical and problem-solving skills. Education: Bachelor's or Master's degree in Computer Science, Data Science, AI, Electrical Engineering, or related quantitative field. Quadratyx is an equal opportunity employer - we will never differentiate candidates on the basis of religion, caste, gender, language, disabilities or ethnic group. Quadratyx reserves the right to place/move any candidate to any company location, partner location or customer location globally, in the best interest of Quadratyx business.
This role is responsible for managing the client expectations. Strategize with various stakeholders to meet customer requirements. KEY RESPONSIBILITIES Data Science: Develop machine learning models to support recommendation systems and NLP projects; provide actionable insights for product and service optimization. Data Engineering: Build and maintain scalable ETL pipelines, optimize data storage solutions (data lakes, columnar formats), and ensure data accuracy for analytics. Data Analysis and Insight Generation: Skilled in analyzing complex datasets to uncover trends and patterns; generate and present insights that drive strategic decisions and enhance client services. Stakeholder Collaboration: Work with product and service teams to understand data needs and translate them into technical solutions. Technical Skills Proficiency with Python (Pandas, NumPy), SQL, and Java. Experience with LLMs, Lang Chain, and Generative AI technologies. Familiarity with ML frameworks (TensorFlow, PyTorch) and data engineering tools (Spark, Kafka). Microservices, CI CD, ML Strong data analysis skills and ability to present findings to both technical and non-technical stakeholders. Proficient understanding of key data engineering concepts, such as data lakes, columnar formats, ETL tools, and BI tools. Knowledge in Machine Learning, NLP, Recommender systems, personalization, Segmentation, microservices architecture and API development. Ability to adapt to a fast-paced, dynamic work environment and learn new technologies quickly. Soft Skills Work in a team/ Independently. Excellent Written & Verbal Communication Skills Solid critical thinking and questioning skills. High degree of flexibility - willing to fill in the gaps rather than relying on others Strong communication skills, especially in presenting data insights. Flexibility, problem-solving, and a proactive approach in a fast-paced environment . Required Educational Qualification & Relevant Experience Bachelors Degree in Computer Science, Data Analytics, Engineering, or a related field. Minimum of 3 to 5 years of experience in data science and data engineering. Strong critical thinking abilities and the capacity to work autonomously. Proficient understanding of key data engineering concepts, such as data lakes, columnar formats, ETL tools, and BI tools High motivation, good work ethic, maturity and personal initiative. Strong oral and written communication skills.
Position: Engineering Manager (Hands-on) AI Products Location: Hyderabad Experience: 8-12 years About the Role: We are building an AI-driven SaaS platform that combines GenAI capabilities, business workflows, and rule-based automation for multiple domains. Were looking for a hands-on technical leader who can design the architecture, guide the engineering team, and bring modern development and automation practices to life. Responsibilities: Architect, design, and implement scalable modules integrating AI/GenAI components. Lead by example in development practices — design reviews, code reviews, testing automation. Drive CI/CD, containerization, and test automation culture. Evaluate and integrate new open-source tools to accelerate development and reduce technical debt. Collaborate with AI teams, product managers, and DevOps for seamless end-to-end delivery. Build for SaaS and on-premise deployment models with security and scalability in mind. Requirements: Strong proficiency in backend (Python, Java, or Node.js) and frontend (React/Angular). Experience with GenAI tools (LangChain, OpenAI APIs, LlamaIndex, etc.). Familiarity with workflow engines and rule-based systems. Solid understanding of Docker, Kubernetes, CI/CD pipelines, and cloud environments (Azure/AWS/GCP). Hands-on experience in test automation and developer-owned QA practices. Strong communication and mentoring skills; able to bridge product and engineering. Nice to Have: Exposure to MLOps or AI model lifecycle management. Experience in low-code/no-code workflow platforms or API integration products. What We Offer: An opportunity to shape the next generation of AI-powered products, work in a flat, innovation-driven environment, and build systems that empower developers and businesses alike.