Architecture design, total solution design from requirements analysis, design and engineering for data ingestion, pipeline, data preparation & orchestration, applying the right ML algorithms on the data stream and predictions.
Responsibilities:
- Defining, designing and delivering ML architecture patterns operable in native and hybrid cloud architectures.
- Research, analyze, recommend and select technical approaches to address challenging development and data integration problems related to ML Model training and deployment in Enterprise Applications.
- Perform research activities to identify emerging technologies and trends that may affect the Data Science/ ML life-cycle management in enterprise application portfolio.
- Implementing the solution using the AI orchestration
Requirements:
- Hands-on programming and architecture capabilities in Python, Java,
- Minimum 6+ years of Experience in Enterprise applications development (Java, . Net)
- Experience in implementing and deploying
- Experience in building Data Pipeline, Data cleaning, Feature Engineering, Feature Store
- Experience in Data Platforms like Databricks, Snowflake, AWS/Azure/GCP Cloud and Data services
- Machine Learning solutions (using various models, such as Linear/Logistic Regression, Support Vector Machines, (Deep) Neural Networks, Hidden Markov Models, Conditional Random Fields, Topic Modeling, Game Theory, Mechanism Design, etc. )
- Strong hands-on experience with statistical packages and ML libraries (e. g. R, Python scikit learn, Spark MLlib, etc. )
- Experience in effective data exploration and visualization (e. g. Excel, Power BI, Tableau, Qlik, etc. )
- Extensive background in statistical analysis and modeling (distributions, hypothesis testing, probability theory, etc. )
- Hands on experience in RDBMS, NoSQL, big data stores like: Elastic, Cassandra, Hbase, Hive, HDFS
- Work experience as Solution Architect/Software Architect/Technical Lead roles
- Experience with open-source software.
- Excellent problem-solving skills and ability to break down complexity.
- Ability to see multiple solutions to problems and choose the right one for the situation.
- Excellent written and oral communication skills.
- Demonstrated technical expertise around architecting solutions around AI, ML, deep learning and related technologies.
- Developing AI/ML models in real-world environments and integrating AI/ML using Cloud native or hybrid technologies into large-scale enterprise applications.
- In-depth experience in AI/ML and Data analytics services offered on Amazon Web Services and/or Microsoft Azure cloud solution and their interdependencies.
- Specializes in at least one of the AI/ML stack (Frameworks and tools like MxNET and Tensorflow, ML platform such as Amazon SageMaker for data scientists, API-driven AI Services like Amazon Lex, Amazon Polly, Amazon Transcribe, Amazon Comprehend, and Amazon Rekognition to quickly add intelligence to applications with a simple API call).
- Demonstrated experience developing best practices and recommendations around tools/technologies for ML life-cycle capabilities such as Data collection,
- Data preparation, Feature Engineering, Model Management, MLOps, Model Deployment approaches and Model monitoring and tuning.
- Back end: LLM APIs and hosting, both proprietary and open-source solutions, cloud providers, ML infrastructure
- Orchestration: Workflow management such as LangChain, Llamalndex, HuggingFace, OLLAMA
- Data Management : LLM cache
- Monitoring: LLM Ops tool
- Tools & Techniques: prompt engineering, embedding models, vector DB, validation frameworks, annotation tools, transfer learnings and others
- Pipelines: Gen AI pipelines and implementation on cloud platforms (preference: Azure data bricks, Docker Container, Nginx, Jenkins)
