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.