Job Description
About Birlasoft:
Birlasoft, a powerhouse where domain expertise, enterprise solutions, and digital technologies converge to redefine business processes. We take pride in our consultative and design thinking approach, driving societal progress by enabling our customers to run businesses with unmatched efficiency and innovation. As part of the CK Birla Group, a multibillion-dollar enterprise, we boast a 12,500+ professional team committed to upholding the Group's 162-year legacy. Our core values prioritize Diversity, Equity, and Inclusion (DEI) initiatives, along with Corporate Sustainable Responsibility (CSR) activities, demonstrating our dedication to building inclusive and sustainable communities. Join us in shaping a future where technology seamlessly aligns with purpose.
1. About the Job Looking for AI architect with experience of 12+years in architecture, programming, machine learning solution, implementation and deployment.
2. Job Title AI Architect
3. Experience: 13+years
4. Location: Bangalore ,Noida.
5. Key Responsibilities
Hands-on programming and architecture capabilities in Python, Java, R, or SCALA
Minimum 6+ years of Experience in Enterprise applications development (Java, . Net)
Experience in implementing and deploying
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.
Skills Required Mandate skill are solution architect , Python, Java, R, or SCALA, Enterprise applications development (Java, . Net),Machine Learning solutions, data exploration and visualization and Machine learning solutions.
6. Additional Requirements
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
Basic knowledge in LLM but AI, NLP and deep learning should be strong
Good with Associate architect or solution architect but no for Technical Lead.
Hands on experience on Docker. Building docker images with a model and all its dependent packages into the image.
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.
