Jobs

We are:

A forward-thinking services company at the forefront of AI-native innovation. We partner with enterprise clients to create next-generation, agent-powered workflows engineered to scale in real-world settings. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality.

You are:

An AI Native Engineer with a minimum of 5 years of experience building cloud-native solutions, and deep expertise in designing and deploying agentic systems, especially for enterprise environments. You are a critical thinker that thrives in ambiguity, delivering concrete results by designing, building, and running custom AI agents that augment workflows and scale across modern infrastructure. You’ll help shape the playbook for how enterprises adopt and scale AI-native engineering globally.

The Work:

You’ll embed directly with clients — acting as both technologist and trusted advisor. You’ll partner with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains. Often, these will be completely net new platforms and systems that need to be stitched together in our clients' environments alongside our Ecosystem partners.

Responsibilities:

Agent Architecture and Engineering: Design and engineer enterprise-ready AI agents encompassing retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.

AI Platform Integration: Develop abstraction layers across AI providers (Anthropic, Google, OpenAI, etc. ) to enable seamless integration and enablement.

Cloud-Native Engineering: Leverage containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability to deliver scalable AI-native systems.

Domain-Specific Workflows: Tailor and deploy agentic applications across verticals — e.g., finance,

What you will do

In this role you will build the agentic systems that power Teradata's next generation of AI-driven analytics - intelligent agents that reason over enterprise data, invoke Vantage capabilities as tools, and complete multi-step analytical tasks autonomously.

You will design and implement agent skills and plugins that extend the Teradata agent harness, build evaluation frameworks to measure agent quality, and contribute to the reliability and performance of agent workflows running on Vantage. Your work directly enables customers to move from asking Teradata questions to delegating analytical tasks to AI agents.

Success looks like agents in production that solve real customer problems - with measurable accuracy, latency, and reliability benchmarks that outperform what is possible on Snowflake Cortex or Databricks Genie.

Who you will work with

You will join a focused team of AI engineers within Teradata's AI Apps, Analytics, and UX Engineering organization, working on the most technically ambitious part of the Teradata product roadmap. We ship code, not demos.

This role reports to the Senior Manager of AI Engineering and collaborates closely with Staff AI Engineers, the Agent Platform team, and Product Management. You will have access to Teradata's world-class data infrastructure and the opportunity to shape how enterprise agents work at scale.

What makes you a qualified candidate
3-6 years of software engineering experience with at least 2 years building agentic AI systems in production - not just RAG pipelines or chatbots. Hands-on experience implementing agent skills, plugins, or tools that extend an agent harness (Claude Code, OpenCode, Codex, OpenClaw, LangGraph, or equivalent). Proficiency in Python; Go experience is a strong advantage. Understanding of agent planning loops, tool-calling protocols, function calling APIs (OpenAI, Anthropic, or Google format), and prompt engineering for reliability.
What you will bring
Op

We are:

A forward-thinking services company at the forefront of AI-native innovation. We partner with enterprise clients to create next-generation, agent-powered workflows engineered to scale in real-world settings. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality.

You are:

An AI Native Engineer with a minimum of 5 years of experience building cloud-native solutions, and deep expertise in designing and deploying agentic systems, especially for enterprise environments. You are a critical thinker that thrives in ambiguity, delivering concrete results by designing, building, and running custom AI agents that augment workflows and scale across modern infrastructure. You’ll help shape the playbook for how enterprises adopt and scale AI-native engineering globally.

The Work:

You’ll embed directly with clients — acting as both technologist and trusted advisor. You’ll partner with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains. Often, these will be completely net new platforms and systems that need to be stitched together in our clients' environments alongside our Ecosystem partners.

Responsibilities:

Agent Architecture and Engineering: Design and engineer enterprise-ready AI agents encompassing retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.

AI Platform Integration: Develop abstraction layers across AI providers (Anthropic, Google, OpenAI, etc. ) to enable seamless integration and enablement.

Cloud-Native Engineering: Leverage containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability to deliver scalable AI-native systems.

Domain-Specific Workflows: Tailor and deploy agentic applications across verticals — e.g., finance,

We are:

A forward-thinking services company at the forefront of AI-native innovation. We partner with enterprise clients to create next-generation, agent-powered workflows engineered to scale in real-world settings. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality.

You are:

An AI Native Engineer with a minimum of 5 years of experience building cloud-native solutions, and deep expertise in designing and deploying agentic systems, especially for enterprise environments. You are a critical thinker that thrives in ambiguity, delivering concrete results by designing, building, and running custom AI agents that augment workflows and scale across modern infrastructure. You’ll help shape the playbook for how enterprises adopt and scale AI-native engineering globally.

The Work:

You’ll embed directly with clients — acting as both technologist and trusted advisor. You’ll partner with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational in complex enterprise domains. Often, these will be completely net new platforms and systems that need to be stitched together in our clients' environments alongside our Ecosystem partners.

Responsibilities:

Agent Architecture and Engineering: Design and engineer enterprise-ready AI agents encompassing retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.

AI Platform Integration: Develop abstraction layers across AI providers (Anthropic, Google, OpenAI, etc. ) to enable seamless integration and enablement.

Cloud-Native Engineering: Leverage containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability to deliver scalable AI-native systems.

Domain-Specific Workflows: Tailor and deploy agentic applications across verticals — e.g., finance,

BPX GenAI Squad Data Scientist requirement

Data Scientist AIML P2P3

Experience 6- 8 Years

Skillset Required

Expert level understanding of Machine LearningDeep Learning concepts techniques algorithms

Hands on working experience on building end to end MLDL solutions

Expert level proficiency in Python programming is a prerequisite

Strong understanding of objectoriented programming and design principles

Experienced with common data science packages in Python like Pandas Numpy Matplotlib and frameworks such as Tensorflow Pytorch

Handson experience in RESTful APIs development with FlaskAPI FastAPI

Excellent understanding of NLP concepts libraries including Word2Vec TFIDF NLTK SpaCy Gensim BERT GloVe etc

Must have experience in development of NLPNLGNLU projects

Deep understanding of Transformer Architectures including BERT GPT DistillBert

Good understanding of Generative AI concepts like Large Language Models Finetuning Evaluating LLMs PEFT RAG Embeddings Vector Databases etc

Experience in frameworks such LangChain LlamaIndex

Experience with Large Language models such as Llama GPT PaLM etc

Experience with Amazon Bedrock Azure OpenAI GCP GenAI Studio

Expertise in training and finetuning LLMs would be preferred

Proficientcy in model development deployment and optimization techniques

Certifications in GenAIAIML preferred

Experienced in deploying AI solutions in cloud environments

Working knowledge in any of the major cloud services AWSAzure

Working knowledge in either of AWS SageMakerAzure Cognitive ServicesGoogle Vertex AI

Certification in any of the Cloud Platforms AWSAzureGCP

Good problemsolving and critical thinking skills

Familiarity with version control systems Git

Knowledge of SQL NoSQL

Key Roles Responsibilities

Create and maintain highquality reusable code components and libraries for future use

Perform rigorous testing and validation to ensure models perform accurately and efficiently in various conditions

• *Title: Application Security (AppSec) + AI Engineering
• *Location: Iselin, NJ (Main) and other Cyber prime locations like Minneapolis, MN, Chander AZ, Dallas, TX (Los Colinas), Charlotte, NC
• *Duration: 6 Months Contract with Possible Ext.
• *Onsite Expectation: 3 days/week (Tuesday–Thursday)
• *JD:
• *Core focus:
• *Application Security (AppSec) + AI Engineering
• *Strong Application Security background

Hands-on with AppSec tools like:
• Checkmarx (critical)
• Other SAST/DAST tools

AI-driven Code Security & Remediation (production-level experience)
• Using AI to:
• Auto-generating secure code fixes
• Detect vulnerabilities
• Generate code remediation suggestions
• Integrating AI into security workflows

Tools / Platforms
• OpenAI (PRIMARY focus)
• GitHub Copilot
• Cloud-based AI coding tools

Real Use Cases Expected
• Deploying AI tools within AppSec pipelines
• Integrating AI with tools like Checkmarx
• Automating vulnerability remediation
• Improving developer productivity using AI

Required Tech Stack
• Python
• Exposure to TensorFlow / PyTorch
• Experience with:
• JIRA (tracking)
• Confluence (documentation)
• True Engineer background required

AppSec Tools and AI tooling
• Focus on AppSec AI eng. Deploying AI toolsto complement existing Appsec tools like Checkmarx and Black Duck
• experience in deploying AI tools for AppSec processes, including auto code remediation.
• The role requires experience with models like Copilot, OpenAI, and Entropic Cloud for code remediation.
• Candidates should have production experience implementing AI tools at scale, not just proof of concept (POC) code.
• The role is specific to AppSec AI engineering, not traditional AI engineering roles.
• role involves writing frameworks against AI models to remediate Checkmarx findings, not directly working with Checkmarx queries.
• various AI models.
• *--------
• *Thanks.
• *Regards,
• *Ashish
• *Email ID: ashish@Hireontech.com**

We are:

A forward-thinking services company at the forefront of AI-native innovation. We partner with enterprise clients to create next-generation, agent-powered workflows engineered to scale in real-world settings. Our engineers embed deeply with customers, moving projects beyond experimentation into operational reality.

You Are:

A Forward-Deployed AI Engineer who thrives at the intersection of cutting-edge AI platforms, enterprise systems, and real-world business problems. It is a product-minded, customer-embedded engineering role where you will work directly with clients, partners, and internal product teams to design, prototype, deploy, and scale end-to-end AI solutions.

The Work:

You will work hands-on with platforms such as OpenAI, Anthropic, ServiceNow, and other enterprise systems, translating ambiguous business needs into production-grade AI workflows that deliver measurable impact.

This role requires strong engineering depth, excellent communication, comfort with ambiguity, and a builder’s mindset.

Responsibilities:

Work directly with clients and internal teams to understand business problems, shape AI use cases, and define technical solutions — often from ambiguous or evolving requirements.

Design and implement end-to-end AI solutions, integrating LLMs and AI services (OpenAI, Anthropic, etc.) with enterprise platforms (ServiceNow, Salesforce, data platforms, internal systems).

Rapidly prototype, iterate, and deploy AI-enabled workflows, agents, copilots, and automations into real operational environments.

Serve as the technical bridge between product, engineering, data, and business stakeholders, ensuring solutions are technically sound, usable, and aligned with business value.

Drive solution architecture, including:

Prompt and workflow design

API integrations and orchestration

Enterprise knowledge integration)

Security, privacy, and governance considerations

Own solutions from concept → prototype → production → iteration, with acco