Senior AI Software Engineer

Accountabilities: AI and machine learning solution development: Design, develop, implement, and maintain advanced AI/ML solutions that address client requirements and align with existing product capabilities and business objectives. Generative and agentic AI engineering: Build and integrate end-to-end solutions using generative AI, agentic AI, large language models (LLMs), natural language processing (NLP), retrieval-augmented generation (RAG), and related frameworks. Data analysis and preparation: Analyze client datasets, assess data complexity and consistency, collect information from multiple sources, and perform data cleaning, preprocessing, profiling, and transformation to ensure data quality and reliability. Machine learning model development: Apply appropriate machine learning and deep learning algorithms to identify patterns, generate insights, solve business problems, and improve the performance of AI-powered applications. Data engineering and pipeline management: Design, implement, and maintain data infrastructure, ETL processes, data integration workflows, and machine learning pipelines that support reliable model development and deployment. Software development and API integration: Develop production-ready software components, integrate APIs and external services, and support the implementation of AI solutions within existing applications and enterprise systems. AI application development: Build and maintain applications using technologies such as Python, FastAPI, LangChain, Hugging Face, PySpark, OCR tools, and relevant machine learning libraries. Cloud infrastructure and deployment: Support AI/ML workloads across cloud environments, including Microsoft Azure and AWS, while leveraging relevant infrastructure, containerization, monitoring, and data platform technologies. Performance monitoring and optimization: Develop dashboards and monitoring tools to track key performance indicators, evaluate system reliability, identify operational issues, and recommend improvements to existing machine learning systems. Cross-functional collaboration: Work closely with business stakeholders, clients, data scientists, data engineers, software developers, QA engineers, and DevOps teams to maintain data accuracy and deliver integrated technical solutions. Technical research and innovation: Evaluate emerging AI, machine learning, and data engineering technologies, recommending tools, frameworks, and techniques that improve scalability, maintainability, reliability, and operational efficiency. Insights and stakeholder communication: Conduct ad hoc analyses, communicate technical findings, and present clear, actionable recommendations to technical and non-technical stakeholders. Quality, accountability, and continuous improvement: Maintain high standards of integrity, client service, documentation, and delivery quality while taking ownership of outcomes and identifying opportunities to improve workflows and processes. Requirements: Education: A bachelor's or master's degree in Computer Science, Information Technology, Computer Engineering, or a related discipline. Relevant qualifications such as B.Tech, MCA, or an MS in Computer Science are suitable. Professional experience: At least 5 years of relevant experience in AI/ML implementation, data engineering, software development, or related technical domains, with hands-on experience delivering and maintaining production-oriented solutions. Programming expertise: Strong proficiency in Python, with practical experience in software development, application integration, implementation, and API development. Generative and agentic AI: Hands-on end-to-end development experience with generative AI, agentic AI, LLMs, RAG, and NLP-based applications. AI frameworks and tools: Experience with FastAPI, LangChain, Hugging Face, RAGAS, PySpark, OCR technologies, and relevant NLP libraries and frameworks. Machine learning and deep learning: Strong understanding of machine learning algorithms and their implementation, with familiarity with frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, Keras, and Pandas. Data engineering capabilities: Experience with ETL, data wrangling, data integration, data profiling, data quality management, metadata management, and data discovery. The ability to build and maintain reliable data pipelines is essential. Cloud and DevOps technologies: Practical experience with Microsoft Azure and/or AWS, with familiarity with Docker, Databricks, application monitoring tools such as Application Insights, and cloud-based deployment practices. LLM and API integration: Experience working with large language models, integrating APIs, and connecting AI components with enterprise systems and data sources. Data visualization and reporting: Familiarity with visualization and business intelligence tools such as Power BI or equivalent platforms to monitor performance and communicate insights. AI solution implementation: Demonstrated ability to translate business requirements and complex datasets into scalable, maintainable AI/ML applications that can be integrated into existing products and workflows. Analytical and problem-solving skills: Strong analytical thinking, attention to detail, and the ability to troubleshoot technical challenges, identify data inconsistencies, and optimize system performance. Communication and collaboration: Excellent written and verbal communication skills, with the ability to explain technical concepts clearly and work effectively with clients and multidisciplinary teams. Client-focused mindset: A commitment to understanding client needs, building productive relationships, maintaining confidentiality, and delivering solutions that meet agreed requirements. Ownership and decision-making: A results-oriented approach, with the ability to take initiative, make informed decisions, manage priorities, and remain effective in complex or changing situations. Innovation and adaptability: A willingness to explore emerging technologies, propose improvements, take calculated risks, and continuously develop technical skills. Operational excellence: A strong commitment to quality, documentation, accountability, collaboration, and continuous improvement throughout the software and AI development lifecycle. Benefits: Flexible work arrangements: Opportunities for remote work in India, with hybrid working options available for certain office locations, including Hyderabad and Pune. The exact arrangement may depend on the assigned location and team. Competitive compensation: Salary determined by location-based market rates, relevant skills and experience, interview performance, and internal pay equity. Comprehensive benefits package: Additional employee benefits are available, with specific healthcare, insurance, leave, and other provisions to be confirmed during the recruitment process. Professional development: Access to learning opportunities designed to support technical growth, career progression, and ongoing skills development. Career mobility: Opportunities to explore different career paths and develop expertise across AI, machine learning, software engineering, and data technologies. Meaningful technical challenges: Work on complex AI and data solutions that support client needs and deliver value at scale. Collaborative environment: Partner with multidisciplinary teams in a workplace that emphasizes integrity, client focus, shared success, and continuous improvement. Innovation-focused culture: Apply modern AI platforms and emerging technologies while contributing ideas that improve products, services, and operational processes. Work-life flexibility: A working environment that recognizes the importance of flexibility and life beyond work.