{"id":1726464,"url":"https://alion.io/job/getirwin-software-engineer-iii-python-aiml","title":"Software Engineer III -Python AI/ML","company":{"id":3815016,"name":"Irwin","domain":"getirwin.com","url":"https://alion.io/company/getirwin","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19000,"max_usd":46000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":23},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FastAPI","optional":false},{"name":"Feature Store","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Hugging Face","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"OpenAI","optional":false},{"name":"Pandas","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Spark","optional":false},{"name":"TensorFlow","optional":false},{"name":"Transformers","optional":false},{"name":"Amazon EC2","optional":true},{"name":"Amazon S3","optional":true},{"name":"AWS Lambda","optional":true},{"name":"AWS Step Functions","optional":true},{"name":"Flask","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-09-30T00:00:00Z","employer_posted_date":"2026-09-30","last_verified_at":"2026-10-06T13:48:29Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"FactSet creates flexible, open data and software solutions for over 200,000 investment professionals worldwide, providing instant access to financial data and analytics that investors use to make crucial decisions.\nAt FactSet, our values are the foundation of everything we do. They express how we act and operate, serve as a compass in our decision-making, and play a big rolein how we treat each other, our clients, and our communities. We believe that the best ideas can come from anyone, anywhere, at any time, and that curiosity is the key to anticipatingour clients’ needs and exceeding their expectations.\nYour Team's Impact\nWe’re seeking a passionate and experienced Senior Python & Machine Learning Engineer to join our Data domain team. You’ll work on unique, one-of-a-kind problem statements using advanced GenAI, large language models (LLMs), and modern data engineering frameworks. You will help conceptualize and deliver impactful solutions that push the boundaries of data science and machine learning in finance.\nWhat You'll Do :\nDesign, develop, and deploy sophisticated machine learning and GenAI models to solve complex data problems at scale.\nImplement, optimize, and scale ML solutions using Databricks, Spark, and cloud-native data ecosystems (AWS/Azure/GCP).\nCollaborate with other engineers, product managers, and UX teams to build robust, high-performance Python-based analytics pipelines.\nDevelop and finetune LLMs and generative AI applications for structured and unstructured financial data.\nArchitect data processing workflows leveraging Delta Lake, Feature Stores, and MLOps best practices.\nTranslate cutting-edge research (papers, new ML techniques) into production solutions.\nMentor junior data scientists and engineers on ML, best practices and GenAI.\nWork on one-of-a-kind data challenges, including entity disambiguation, real-time risk analytics, NLP, graph data modeling, and anomaly detection.\nKeep up-to-date with the latest in ML tooling, GenAI, Databricks, and cloud data infrastructure.\nWhat We're Looking For\nBachelor’s/Master’s in Computer Science, Engineering, or related field.\n3-5 years professional experience in ML, Python programming, and data engineering.\nDeep expertise in Python (NumPy, Pandas, PySpark, FastAPI, etc.) and ML frameworks (TensorFlow, PyTorch, Transformers).\nPractical experience with GenAI: training/fine-tuning LLMs (OpenAI, HuggingFace, Google Gemini, etc.), prompt engineering, and retrieval-augmented generation (RAG).\nHands-on experience with Databricks (Workspace, MLflow, Delta Lake, Notebooks).\nStrong knowledge of cloud data platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).\nApplied experience with ETL/ELT, data lakes, real-time streaming (Kafka, Spark Streaming).\nProven track record of tackling cutting-edge data problems at scale - published research or open source contributions a plus.\nFamiliarity with modern MLOps toolchains (MLflow, Airflow, Feature Store, CI/CD).\nEffective communicator with excellent collaboration skills.\nTech Stack:\nPython, PySpark, FastAPI, Flask\nTensorFlow, PyTorch, HuggingFace Transformers\nDatabricks, Delta Lake, MLflow\nAWS/Azure/GCP - S3, Blob Storage, EC2, Lambda, Step Functions\nSQL, NoSQL\nWhat's In It For You\nAt FactSet, our people are our greatest asset, and our culture is our biggest competitive advantage. Being a FactSetter means:\nThe opportunity to join an S&P 500 company with over 45 years of sustainable growth powered by the entrepreneurial spirit of a start-up.\nSupport for your total well-being. This includes health, life, and disability insurance, as well as retirement savings plans and a discounted employee stock purchase program, plus paid time off for holidays, family leave, and company-wide wellness days. \nFlexible work accommodations. We value work/life harmony and offer our employees a range of accommodations to help them achieve success both at work and in their personal lives. \nA global community dedicated to volunteerism and sustainability, where collaboration is always encouraged, and individuality drives solutions. \nCareer progression planning with dedicated time each month for learning and development. \nBusiness Resource Groups open to all employees that serve as a catalyst for connection, growth, and belonging. \nCompany Overview:\nFactSet (NYSE:FDS| NASDAQ:FDS) helps the financial community to see more, think bigger, and work better. Our digital platform and enterprise solutions deliver financial data, analytics, and open technology to more than 8,200 global clients, including over 200,000 individual users. Clients across the buy-side and sell-side, as well as wealth managers, private equity firms, and corporations, achieve more every day with our comprehensive and connected content, flexible next-generation workflow solutions, and client-centric specialized support. As a member of the S&P 500, we are committed to sustainable growth and have been recognized among the Best Places to Work in 2023 by Glassdoor as a Glassdoor Employees’ Choice Award winner. Learn more at www.factset.com and follow us on X and LinkedIn.\nAt FactSet, we celebrate difference of thought, experience, and perspective. 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