Hiring for AI Architect || New Jersey, Remote

Role: AI Engineer

Client: Sodexo

Location: Remote (USA

In collaboration with a cross-functional feature team (AI Product Manager, Data Engineer,

DevOps, etc.), the Senior Data Engineer s objective is to design, build, and optimize

scalable and reliable AI/ML and Generative AI solutions, integrating advanced

algorithms and data pipelines across the product lifecycle (e.g., MVM, MVP,

Industrialization).-----2. Key Responsibilities and Expected Deliverables

Support Scoping and Minimum Viable Model Phases (30%)

• Conduct data and tech due diligence, including State-of-the-Art (SoTA) analysis for

AI/ML and Generative AI solutions.

• Set up exploratory environments using tools like Jupyter Notebook, Databricks, or

MLFlow for rapid prototyping.

• Provide accurate workload estimations for product backlogs, collaborating closely with AI

Product Managers.

Design and Implement Scalable AI/ML and Data Pipelines (50%)

• Develop and optimize end-to-end data pipelines using Apache Kafka, PySpark, Azure

Data Factory, and Databricks to integrate heterogeneous data sources.

• Build and deploy Retrieval-Augmented Generation (RAG) solutions and Large Language

Models (LLMs) like Llama or GPT-3.5, leveraging frameworks such as LangChain,

Hugging Face Transformers, and vector databases (e.g., FAISS, Pinecone).

• Implement anomaly detection frameworks using statistical methods (e.g., Z-score,

control charts) and ML-based approaches (e.g., Isolation Forests, Autoencoders),

optimizing for real-time analytics.

• Containerize AI/ML applications using Docker and Kubernetes to ensure scalable,

consistent deployments across environments.

• Embed monitoring metrics and CI/CD pipelines using Azure DevOps and Git to

streamline model testing, deployment, and monitoring, reducing deployment time.

Drive Innovation and Technology Adoption (20%)

• Conduct technological watch on emerging AI/ML, NLP, and Generative AI technologies,

testing tools like TensorFlow, PyTorch, and Snowflake in use-case contexts.

• Recommend and prototype new market technologies to enhance Sodexo s data-driven

capabilities.

• Participate in selecting service providers through RFPs, focusing on cloud and AI

solutions.

MSc in Data Science, Computer Science, or related field (e.g., Information Technology).

• 8-10 years of relevant experience as a Data Engineer or Data Scientist, with a focus on

AI/ML, Generative AI, and data pipeline development.

Programming Languages

• Expert knowledge of Python (mandatory) with experience in R, PL-SQL, and Spark.
• Familiarity with GraphQL and NoSQL databases (e.g., MongoDB, PostgreSQL) is a plus.

Big Data & Distributed Systems

• Proficient in Apache Spark and Kafka for real-time data streaming and processing.
• Extensive experience with Azure Databricks, Azure Data Factory, and Azure Data Lake

Storage Gen2.

• Skilled in container technologies (Docker, Kubernetes) and microservices architectures.
• Expertise in Snowflake for data warehousing, with knowledge of big data file formats

(e.g., Parquet, Delta).

• Familiarity with AWS (S3, Glue) and Google Cloud Platform (BigQuery) is a plus.

Machine Learning and Analytics

• Deep expertise in ML frameworks (TensorFlow, PyTorch, Scikit-learn) and NLP tools

(NLTK, SpaCy, Hugging Face Transformers).

• Experience with Generative AI, LLMs (e.g., Llama, GPT-3.5), and RAG implementations

using LangChain and vector databases.

• Proficient in anomaly detection (e.g., Isolation Forests, Autoencoders) and predictive

modeling (e.g., XGBoost, Random Forest).

• Skilled in visualization tools like Tableau and Power BI for creating impactful dashboards.
• Experience with MLOps workflows using Azure ML Services, MLFlow, or Azure DevOps.

Cloud

• Expert in Azure services (Data Factory, Databricks, Cognitive Search) with a focus on

data and AI solutions.

• Strong understanding of cloud governance, security, and networking (e.g., resource

groups, SSO).

• Knowledge of AWS and Google Cloud Platform is a plus.

Software Engineering

• Proficient in Git for version control and Azure DevOps for CI/CD pipeline creation.
• Strong ability to write clean, modular code and document infrastructure for team

collaboration.

Hard Skills

• System design and data pipeline architecture
• Data modeling, ETL processes, and data integration
• AI/ML model development, including NLP and Generative AI
• Anomaly detection and real-time analytics
• Data visualization and dashboard creation
• DevOps and MLOps practices
• Agile methodology and user story creation
• Validation, testing, and performance optimization

Soft Skills

• Collaborates (Level 3: Leading collaboration across teams)
• Ensures accountability (Level 3: Driving team accountability)
• Drives results (Level 3: Consistently delivering high-impact outcomes)
• Nimble learning (Level 3: Proactively adopting new technologies)