Direct client :: 1. Developer Engineer 2. Generative AI Engineer 3.Data Engineer // Miami, FL or Dallas, TX

[Sai kumar Opsaway]

Hi All we have multiple Direct Client Openings

Locations: Miami, FL or Dallas, TX only (Candidates must be willing to work from either of these locations only)

Work Mode: Hybrid (Candidates are required to work from the office at least 2 days a week)

Duration: 6+ Months Contract

Exp: 12+

Need Locals only

#Position 1:  Rate: $50-55/hr on C2C

Role: Developer Engineer

Key responsibilities include the following:
Primarily involved in DevOps Automation & Tools adoption activities like Jira, Bit Bucket, CI/CD, release management, ChatOps. Typically, this role:
Manages the SCM tool, Build and deployment scripts, and the Non-production Environments
Builds the code from SCM and create binaries
Deploys the binaries in the respective environments
Debugs and fixes any issues related to the environment
Orchestrates the CI-CD pipeline flow for the applications
Creates robust CI-CD pipelines to adapt various technology with tools like Jenkins, Teamcity, Bamboo, uDeploy
Adapts Branching strategy in the source code versioning for better tracking of releases
Implements Quality checks as part of the CI-CD pipeline to produce stable code to the users
Implements Continuous Testing to test the modules in the faster pace of CI-CD cycle
Implements single click deployment for the higher environment for the faster/quicker deployments
Drives adoption of Technology automation and CI/CD across all channels
Is responsible for working with the application development teams to accelerate and drive CI/CD and release management implementation, rollout and adoption
Understands technology industry trends, how they impact platform automation future solutions and provides strategic direction

#Position 2:  Rate: $50-55/hr on C2C

Role: Generative AI Engineer 

7+ years of overall software engineering or data engineering experience.
3+ years of hands-on experience in Artificial Intelligence and Machine Learning.
Strong experience with Generative AI technologies and Large Language Models (LLMs).
Proficiency in Python and AI application development.
Hands-on experience with:
OpenAI, Azure OpenAI, Claude, Gemini, or similar LLM platforms
LangChain, LangGraph, LlamaIndex, or equivalent AI orchestration frameworks
Retrieval-Augmented Generation (RAG) architectures
Prompt Engineering and LLM optimization techniques
Vector Databases such as Pinecone, ChromaDB, Weaviate, Milvus, or FAISS
Experience building REST APIs and AI-powered microservices.
Strong knowledge of data processing and ETL pipelines.
Experience with SQL and NoSQL databases.
Familiarity with cloud platforms such as Azure, AWS, or Google Cloud Platform.
Experience with Git, CI/CD pipelines, and DevOps practices.
Prior experience in Banking, Financial Services, or highly regulated environments.
Experience with AI governance, responsible AI, model monitoring, and security best practices.
Knowledge of MLOps and model deployment frameworks.
Experience with containerization technologies such as Docker and Kubernetes.
Exposure to machine learning frameworks including TensorFlow, PyTorch, or Hugging Face.
Experience integrating AI solutions with enterprise applications and business workflows.
Design, develop, and deploy enterprise-scale Generative AI solutions.
Build and optimize RAG-based applications leveraging internal and external knowledge sources.
Develop AI assistants, chatbots, intelligent search, document processing, and workflow automation solutions.
Integrate LLMs with enterprise systems, APIs, and data platforms.
Implement prompt engineering, model evaluation, and performance optimization strategies.
Collaborate with business stakeholders to identify and deliver high-value AI use cases.
Ensure security, scalability, compliance, and governance standards are met.
Participate in architecture reviews, code reviews, testing, and production support activities.
Stay current with emerging Generative AI technologies and industry best practices.
Python
Generative AI / LLMs
OpenAI / Azure OpenAI / Claude / Gemini
LangChain / LangGraph / LlamaIndex
RAG Architecture
Vector Databases
REST APIs
SQL / NoSQL
Azure / AWS / GCP
Docker / Kubernetes
Git / CI-CD
 

#Position 3: 

Role: Data Engineer

9 + years of experience in Data Engineering and Data Warehousing.
Strong proficiency in SQL and data modeling concepts.
Hands-on experience with Python, PySpark, or Scala for data processing.
Extensive experience with ETL/ELT development and data integration.
Strong experience with cloud-based data platforms such as Azure, AWS, or GCP.
Experience with Azure Data Factory (ADF), Databricks, Snowflake, or equivalent modern data platforms.
Knowledge of data warehousing concepts, dimensional modeling, and data lake architectures.
Experience developing and optimizing large-scale data pipelines.
Familiarity with orchestration tools such as Airflow, Control-M, or similar platforms.
Experience with Git, CI/CD pipelines, and Agile development methodologies.
Prior experience in Banking, Financial Services, or highly regulated environments.
Experience with real-time data processing using Kafka, Spark Streaming, or similar technologies.
Exposure to AI/ML data pipelines and analytics platforms.
Experience with data governance, data quality, and metadata management tools.
Knowledge of containerization technologies such as Docker and Kubernetes is a plus.
Design, develop, and maintain scalable ETL/ELT pipelines and data integration solutions.
Build and optimize data ingestion, transformation, and loading processes.
Develop and maintain enterprise data warehouses, data lakes, and cloud-based data platforms.
Collaborate with business analysts, data scientists, and application teams to understand data requirements.
Ensure data quality, integrity, security, and compliance across enterprise systems.
Monitor, troubleshoot, and optimize data workflows for performance and reliability.
Support reporting, analytics, regulatory, and AI/ML initiatives through robust data engineering solutions.
Participate in architecture reviews, code reviews, testing, and production support activities.
SQL
Python / PySpark
Azure Data Factory (ADF)
Databricks
Snowflake
Data Warehousing
Data Lakes
Azure / AWS / GCP
Kafka
Spark
Airflow
Git
CI/CD
Experience supporting enterprise-scale financial data platforms.
Exposure to Generative AI, Machine Learning, or Advanced Analytics data ecosystems.