Note: The job is a remote job and is open to candidates in USA. SPECTRAFORCE is seeking a Senior Machine Learning Engineer to support strategic data science and artificial intelligence initiatives. The role focuses on developing, training, testing, deploying, and productionizing machine learning models, generative AI solutions, and agentic AI systems while partnering with engineering and data teams.
Responsibilities
- Responsible for the hands-on development of advanced Data Science, Machine Learning and Agentic AI solutions comprising the portfolio developed by the Lead Data Scientist and the technical requirements specified by the Lead Data Scientist. Perform hands-on data analysis, model development, model training, model testing and development of Agentic AI systems
- Write production grade code and partner with machine learning engineers to push model code into production including traditional machine learning, statistical models, GenAI and agentic solutions
- Experience with engineering of Agentic AI systems, fine-tuning techniques (ex: LoRA), deployment of LLMs, RAG, Agentic RAG, Strands, Claude Agents SDK, Agents SDK and Agentic AI concepts
- Partner with machine learning engineers to productionized machine learning models and agents. Partner with data engineers to build data pipelines. Partner with software engineers to integrate solutions with business platforms
- Continuously research new methods for problem solution, including new algorithms, modeling techniques, and data analytics techniques
- Experience in using Cloud based AI Platforms like Bedrock and Sagemaker AI
Skills
- Advanced degree (Masters, Ph.D.) in Mathematics, Statistics, Engineering, Econometrics, Physics, Computer Science, Actuarial, Data Science, or comparable quantitative disciplines
- Working on complex problems in which analysis of situations or data requires an in-depth evaluation of various factors. Exercises judgment within broadly defined practices and policies in selecting methods, techniques and evaluation criteria for obtaining results
- Ability to learn new skills and knowledge on an ongoing basis through self-initiative and seeking challenges
- Excellent problem solving, communication and collaboration skills
- Data Acquisition and Transformation : Acquiring data from disparate data sources using API's, semantic data models, and SQL. Transform data using SQL and Python. Visualizing data using a diverse tool set including but not limited to Python
- Database Management System : Knowledge of how databases are structured and function in order to use them efficiently. May include multiple data environments, cloud/AWS, primary and foreign key relationships, table design, database schemas, SQL (relational), Unstructured (NoSQL), Graph/ontology (Graph DB), semantic data models
- Data Analysis and Insights : Analyzing structured and unstructured data using data visualization, manipulation, and statistical methods to identify patterns, anomalies, relationships, and trends
- Statistics and Computing : Exceptional understanding of: Multivariable Calculus, Linear Algebra, Differential Equations, Applied Probability, Applied Statistics, Computer Science (Programming Methodologies), and Cloud. Knowledge of statistical techniques such as the use of descriptive, inferential, Bayesian statistics, time series analysis to extract business insights and experimentation to solve business problems
- Machine Learning : Understanding of machine learning theory, including the mathematics underlying machine learning algorithms. Expertise in the application of machine learning theory to building, training, testing, interpreting and monitoring machine learning models (supervised models including regression and classification. Unsupervised models including clustering/segmentation and anomaly detection)
- Generative AI & Natural Language Processing : Experience with modeling and interpreting text analysis including NLP, LLMs, and Generative AI. Experience in modern Gen AI technologies including RAG, LangChain, LangGraph, vector DB, LangFuse, AgentCore, Agents, and their application in Retirement Strategies area
- Model Deployment: Understanding of model development life cycle, A/B testing, CI/CD pipelines, and pipeline frameworks such as AWS SageMaker, and newer AWS/Azure Agentic AI infrastructure products
- Data Wrangling : Preparing data for further analysis; Redefining and mapping raw data to generate insights; Processing of large datasets (structured, unstructured)
- DevSecOps : Knowledge in the project development life cycle in an AWS environment. Familiar with development, QA, staging and production deployment stages
- Programming Languages : Python, SQL
Benefits
- Remote work
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