Key Responsibilities:

AI Agentic Engineering

<ul>
<li style="margin-left:0px;">Design, architect, and develop multi-agent AI systems that exhibit autonomy, memory, goal-orientation, tool-use, and reasoning.</li>
</ul>

<ul>
<li style="margin-left:0px;">Integrate LLMs (e.g., GPT-4/5, Claude, Gemini) with tools (APIs, databases, code execution) using frameworks such as LangChain, AutoGen, CrewAI, or custom stacks.</li>
</ul>

<ul>
<li style="margin-left:0px;">Implement agent coordination, planning, communication, and long-term memory management.</li>
</ul>

<ul>
<li style="margin-left:0px;">Develop secure, scalable agent orchestration layers for real-world applications like AI research assistants, customer support agents, financial decision agents, and workflow automation.</li>
</ul>

Generative AI Engineering

<ul>
<li style="margin-left:0px;">Fine-tune and deploy LLMs and diffusion models (e.g., LLaMA, Mistral, Stable Diffusion, DALL•E, Claude).</li>
</ul>

<ul>
<li style="margin-left:0px;">Build RAG (Retrieval-Augmented Generation) pipelines with vector stores and semantic search tools (e.g., FAISS, Pinecone, Weaviate).</li>
</ul>

<ul>
<li style="margin-left:0px;">Develop custom generative workflows using transformers for NLP/NLU tasks: summarization, synthesis, dialogue, reasoning, code generation, etc.</li>
</ul>

<ul>
<li style="margin-left:0px;">Experiment with multi-modal models for vision-language tasks (e.g., CLIP, Flamingo, Gemini 1.5).</li>
</ul>

Data Science &amp; Machine Learning

<ul>
<li style="margin-left:0px;">Perform advanced statistical analysis, machine learning modeling, and causal inference using Python/R.</li>
</ul>

<ul>
<li style="margin-left:0px;">Work with large, diverse, structured and unstructured datasets to build ML models for classification, regression, clustering, recommendation, etc.</li>
</ul>

<ul>
<li style="margin-left:0px;">Deploy pipelines using tools like Airflow, MLflow, SageMaker, Vertex AI, or Databricks.</li>
</ul>

<ul>
<li style="margin-left:0px;">Collaborate with stakeholders to drive data-driven decision-making and AI adoption in enterprise workflows.</li>
</ul>

Minimum Qualifications:

<ul>
<li style="margin-left:0px;">Bachelor /Masters in Computer Science, Machine Learning, AI, Data Science, or related field.</li>
</ul>

<ul>
<li style="margin-left:0px;">4+ years of hands-on experience in AI/ML development, data science, and software engineering.</li>
</ul>

<ul>
<li style="margin-left:0px;">Deep understanding of transformers, attention mechanisms, reinforcement learning, embeddings, and generative modeling.</li>
</ul>

<ul>
<li style="margin-left:0px;">Solid Python development experience; familiarity with LangChain, PyTorch, Hugging Face, Ray, FastAPI.</li>
</ul>

<ul>
<li style="margin-left:0px;">Strong grounding in probability, statistics, optimization, and algorithm design.</li>
</ul>

<ul>
<li style="margin-left:0px;">Experience deploying LLMs in production environments (cloud, API, containerized microservices).</li>
</ul>

Preferred Skills:

<ul>
<li style="margin-left:0px;">Experience with RLHF (Reinforcement Learning with Human Feedback) or fine-tuning open-source LLMs.</li>
</ul>

<ul>
<li style="margin-left:0px;">Familiarity with memory &amp; long-context agents, vector databases, and knowledge graphs.</li>
</ul>

<ul>
<li style="margin-left:0px;">Knowledge of MLOps best practices, CI/CD, and infrastructure-as-code (e.g., Terraform, Docker, Kubernetes).</li>
</ul>

<ul>
<li style="margin-left:0px;">Hands-on with LLMOps tools (e.g., Weights &amp; Biases, PromptLayer, LangSmith, Trulens).</li>
</ul>

<ul>
<li style="margin-left:0px;">Understanding of data privacy, fairness, and model interpretability.</li>
</ul>

Soft Skills:

<ul>
<li style="margin-left:0px;">Strong problem-solving ability and curiosity.</li>
</ul>

<ul>
<li style="margin-left:0px;">Excellent communication skills, especially explaining complex AI concepts to non-technical stakeholders.</li>
</ul>

<ul>
<li style="margin-left:0px;">Passion for building autonomous systems that can think, plan, and learn.</li>
</ul>

<ul>
<li style="margin-left:0px;">Collaborative mindset, with a deep respect for interdisciplinary knowledge sharing.</li>
</ul>

Data Scientist

Key Responsibilities: AI Agentic Engineering Design, architect, and develop multi-agent AI systems that exhibit autonomy, memory, goal-orientation, tool-use, and reasoning. Integrate LLMs (e.g., GPT-4/5, Claude, Gemini) with tools (APIs, databases, code execution) using frameworks such as LangChain, AutoGen, CrewAI, or custom stacks. Implement agent coordination, planning, communication, and long-term memory management. Develop secure, scalable agent orchestration layers for real-world applications like AI research assistants, customer support agents, financial decision agents, and workflow automation. Generative AI Engineering Fine-tune and deploy LLMs and diffusion models (e.g., LLaMA, Mistral, Stable Diffusion, DALL•E, Claude). Build RAG (Retrieval-Augmented Generation) pipelines with vector stores and semantic search tools (e.g., FAISS, Pinecone, Weaviate). Develop custom generative workflows using transformers for NLP/NLU tasks: summarization, synthesis, dialogue, reasoning, code generation, etc. Experiment with multi-modal models for vision-language tasks (e.g., CLIP, Flamingo, Gemini 1.5). Data Science &amp; Machine Learning Perform advanced statistical analysis, machine learning modeling, and causal inference using Python/R. Work with large, diverse, structured and unstructured datasets to build ML models for classification, regression, clustering, recommendation, etc. Deploy pipelines using tools like Airflow, MLflow, SageMaker, Vertex AI, or Databricks. Collaborate with stakeholders to drive data-driven decision-making and AI adoption in enterprise workflows. Minimum Qualifications: Bachelor /Masters in Computer Science, Machine Learning, AI, Data Science, or related field. 4+ years of hands-on experience in AI/ML development , data science , and software engineering. Deep understanding of transformers , attention mechanisms, reinforcement learning, embeddings, and generative modeling. Solid Python development experience; familiarity with LangChain, PyTorch, Hugging Face, Ray, FastAPI. Strong grounding in probability, statistics, optimization, and algorithm design. Experience deploying LLMs in production environments (cloud, API, containerized microservices). Preferred Skills: Experience with RLHF (Reinforcement Learning with Human Feedback) or fine-tuning open-source LLMs. Familiarity with memory &amp; long-context agents, vector databases, and knowledge graphs. Knowledge of MLOps best practices, CI/CD, and infrastructure-as-code (e.g., Terraform, Docker, Kubernetes). Hands-on with LLMOps tools (e.g., Weights &amp; Biases, PromptLayer, LangSmith, Trulens). Understanding of data privacy, fairness, and model interpretability. Soft Skills: Strong problem-solving ability and curiosity. Excellent communication skills, especially explaining complex AI concepts to non-technical stakeholders. Passion for building autonomous systems that can think, plan, and learn. Collaborative mindset, with a deep respect for interdisciplinary knowledge sharing.

Data Scientist in Gurugram

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Job Description

Job Description for Data Scientist

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